ICML UDL Evaluating Deep Learning Models Applications to NLP Nazneen Rajani.pdf
•
0 likes•5 views
This document discusses evaluating deep learning models, specifically for natural language processing tasks. It outlines the current status quo of evaluation, which focuses on aggregate performance but can miss failures on specific examples or distributions. The document then introduces Robustness Gym and SummVis as tools to help with more rigorous evaluation. Robustness Gym allows consolidated and fine-grained evaluation to better expose model vulnerabilities and inform next steps like further analysis or patching. SummVis helps evaluate text summarization models while addressing issues like input contamination between pre-training and evaluation data. The goal of more robust evaluation is to gain a fuller picture of model performance and drive iterative improvement.
Report
Share
Report
Share
Download to read offline
Recommended
Grammatical Error Correction with Improved Real-world Applicability
The document discusses grammatical error correction with improved real-world applicability. It discusses three main issues with current grammatical error correction:
[1] Evaluation - Current single corpus evaluations are not reliable and do not reflect real-world scenarios with varying proficiency levels.
[2] Data noise - There is noise in existing grammatical error correction training data that can negatively impact model performance.
[3] Low resource - Current approaches require large amounts of data and model parameters, which has low real-world applicability.
The document proposes approaches to address these issues, including cross-sectional evaluation using multiple test sets, a self-refinement strategy to reduce data noise, and analyzing grammatical generalization
Promise 2011: "A Principled Evaluation of Ensembles of Learning Machines for ...
The document discusses evaluating ensembles of learning machines for software effort estimation. It aims to determine if ensemble methods improve upon single learners, which ensembles perform best, and how to select models for different datasets. The study uses several public software effort estimation datasets and evaluates multiple ensemble techniques, including bagging and negative correlation learning, against single learners like decision trees. Statistical tests are used to rigorously compare the performance of different models.
ACM ICTIR 2019 Slides - Santa Clara, USA
This document proposes a novel weak supervision approach to unify explicit and implicit feedback for rating prediction and ranking recommendation tasks. It trains an explicit feedback model to annotate implicit feedback with predicted ratings. This allows training a new model on the annotated data, improving ranking accuracy while increasing coverage of long-tail items compared to baselines. Evaluation on multiple datasets shows the approach enhances recommendation for both rating prediction and ranking, with less popularity bias than models using only explicit or implicit feedback.
A Validation of Object-Oriented Design Metrics as Quality Indicators
The document summarizes a research paper that empirically validated several object-oriented design metrics proposed by Chidamber and Kemerer as indicators of fault-prone classes. The study analyzed 6 metrics on 180 classes from a system. Univariate analysis found 5 metrics to be significantly correlated with fault probability. Multivariate analysis using these 5 metrics achieved better prediction of faulty classes than models using traditional code metrics. The research validated that these OO design metrics can help identify fault-prone classes early in the development lifecycle.
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...
This document describes an empirical study that compares different context-aware recommendation approaches. It evaluates three context-aware splitting approaches (item splitting, user splitting, and UI splitting) on several datasets using different recommendation algorithms and impurity criteria for splitting. The results show that UI splitting generally performs the best when used with matrix factorization as the recommendation algorithm. The document also compares the splitting approaches to other context-aware recommendation methods like differential context modeling and context-aware matrix factorization. The goal is to better understand how different context-aware techniques compare and which may be most appropriate depending on the data and application.
ExplainingMLModels.pdf
Integrated Gradients provides a method for attributing the predictions of machine learning models to features of the input. It works by calculating the gradient of the model output with respect to the input across all points along the linear path between a baseline input and the actual input. This path integral attribution method satisfies several desirable properties. Integrated Gradients can be used for applications like generating explanations, debugging models, and analyzing model robustness.
2cee Master Cocomo20071
21st century effort estimation methods from JPL. Jairus Hihn (JPL), Tim Menzies (WVU), Karen Lum (JPL), Dan Baker (WVU)
LAK21 Data Driven Redesign of Tutoring Systems (Yun Huang)
This is the slides for our paper in LAK '21 conference:
Yun Huang, Nikki G. Lobczowski, J. Elizabeth Richey, Elizabeth A. McLaugh- lin, Michael W. Asher, Judith M. Harackiewicz, Vincent Aleven, and Kenneth R. Koedinger. 2021. A General Multi-method Approach to Data-Driven Re- design of Tutoring Systems. In LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12–16, 2021, Irvine, CA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3448139.3448155
Abstract: Analytics of student learning data are increasingly important for continuous redesign and improvement of tutoring systems and courses. There is still a lack of general guidance on converting analytics into better system design, and on combining multiple methods to maximally improve a tutor. We present a multi-method approach to data-driven redesign of tutoring systems and its empirical evaluation. Our approach systematically combines existing and new learning analytics and instructional design methods. In particular, our methods involve identifying difficult skills and creating focused tasks for learning these difficult skills effectively following content redesign strategies derived from analytics. In our past work, we applied this approach to redesigning an algebraic modeling unit and found initial evidence of its effectiveness. In the current work, we extended this approach and applied it to redesigning two other tutor units in addition to a second iteration of redesigning the previously redesigned unit. We conducted a one-month classroom experiment with 129 high school students. Compared to the origi- nal tutor, the redesigned tutor led to significantly higher learning outcomes, with time mainly allocated to focused tasks rather than original full tasks. Moreover, it reduced over- and under-practice, yielded a more effective practice experience, and selected skills progressing from easier to harder to a greater degree. Our work provides empirical evidence of the effectiveness and generality of a multi-method approach to data-driven instructional redesign.
Recommended
Grammatical Error Correction with Improved Real-world Applicability
The document discusses grammatical error correction with improved real-world applicability. It discusses three main issues with current grammatical error correction:
[1] Evaluation - Current single corpus evaluations are not reliable and do not reflect real-world scenarios with varying proficiency levels.
[2] Data noise - There is noise in existing grammatical error correction training data that can negatively impact model performance.
[3] Low resource - Current approaches require large amounts of data and model parameters, which has low real-world applicability.
The document proposes approaches to address these issues, including cross-sectional evaluation using multiple test sets, a self-refinement strategy to reduce data noise, and analyzing grammatical generalization
Promise 2011: "A Principled Evaluation of Ensembles of Learning Machines for ...
The document discusses evaluating ensembles of learning machines for software effort estimation. It aims to determine if ensemble methods improve upon single learners, which ensembles perform best, and how to select models for different datasets. The study uses several public software effort estimation datasets and evaluates multiple ensemble techniques, including bagging and negative correlation learning, against single learners like decision trees. Statistical tests are used to rigorously compare the performance of different models.
ACM ICTIR 2019 Slides - Santa Clara, USA
This document proposes a novel weak supervision approach to unify explicit and implicit feedback for rating prediction and ranking recommendation tasks. It trains an explicit feedback model to annotate implicit feedback with predicted ratings. This allows training a new model on the annotated data, improving ranking accuracy while increasing coverage of long-tail items compared to baselines. Evaluation on multiple datasets shows the approach enhances recommendation for both rating prediction and ranking, with less popularity bias than models using only explicit or implicit feedback.
A Validation of Object-Oriented Design Metrics as Quality Indicators
The document summarizes a research paper that empirically validated several object-oriented design metrics proposed by Chidamber and Kemerer as indicators of fault-prone classes. The study analyzed 6 metrics on 180 classes from a system. Univariate analysis found 5 metrics to be significantly correlated with fault probability. Multivariate analysis using these 5 metrics achieved better prediction of faulty classes than models using traditional code metrics. The research validated that these OO design metrics can help identify fault-prone classes early in the development lifecycle.
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...
This document describes an empirical study that compares different context-aware recommendation approaches. It evaluates three context-aware splitting approaches (item splitting, user splitting, and UI splitting) on several datasets using different recommendation algorithms and impurity criteria for splitting. The results show that UI splitting generally performs the best when used with matrix factorization as the recommendation algorithm. The document also compares the splitting approaches to other context-aware recommendation methods like differential context modeling and context-aware matrix factorization. The goal is to better understand how different context-aware techniques compare and which may be most appropriate depending on the data and application.
ExplainingMLModels.pdf
Integrated Gradients provides a method for attributing the predictions of machine learning models to features of the input. It works by calculating the gradient of the model output with respect to the input across all points along the linear path between a baseline input and the actual input. This path integral attribution method satisfies several desirable properties. Integrated Gradients can be used for applications like generating explanations, debugging models, and analyzing model robustness.
2cee Master Cocomo20071
21st century effort estimation methods from JPL. Jairus Hihn (JPL), Tim Menzies (WVU), Karen Lum (JPL), Dan Baker (WVU)
LAK21 Data Driven Redesign of Tutoring Systems (Yun Huang)
This is the slides for our paper in LAK '21 conference:
Yun Huang, Nikki G. Lobczowski, J. Elizabeth Richey, Elizabeth A. McLaugh- lin, Michael W. Asher, Judith M. Harackiewicz, Vincent Aleven, and Kenneth R. Koedinger. 2021. A General Multi-method Approach to Data-Driven Re- design of Tutoring Systems. In LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12–16, 2021, Irvine, CA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3448139.3448155
Abstract: Analytics of student learning data are increasingly important for continuous redesign and improvement of tutoring systems and courses. There is still a lack of general guidance on converting analytics into better system design, and on combining multiple methods to maximally improve a tutor. We present a multi-method approach to data-driven redesign of tutoring systems and its empirical evaluation. Our approach systematically combines existing and new learning analytics and instructional design methods. In particular, our methods involve identifying difficult skills and creating focused tasks for learning these difficult skills effectively following content redesign strategies derived from analytics. In our past work, we applied this approach to redesigning an algebraic modeling unit and found initial evidence of its effectiveness. In the current work, we extended this approach and applied it to redesigning two other tutor units in addition to a second iteration of redesigning the previously redesigned unit. We conducted a one-month classroom experiment with 129 high school students. Compared to the origi- nal tutor, the redesigned tutor led to significantly higher learning outcomes, with time mainly allocated to focused tasks rather than original full tasks. Moreover, it reduced over- and under-practice, yielded a more effective practice experience, and selected skills progressing from easier to harder to a greater degree. Our work provides empirical evidence of the effectiveness and generality of a multi-method approach to data-driven instructional redesign.
Journal club: Meta-Prod2Vec
This document discusses Meta-Prod2Vec, a method for product recommendation that uses product embeddings learned from both co-purchase data and side information (metadata). It extends Prod2Vec, which learns embeddings from co-purchase data alone. Meta-Prod2Vec uses a loss function that incorporates both embedding similarities and metadata similarities to generate recommendations. The experiments show Meta-Prod2Vec improves recommendations for cold start products and performs best when combined with traditional collaborative filtering in an ensemble model.
Landscape of AI/ML in 2023
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Recommenders, Topics, and Text
Recommenders, Topics, and Text
Lecture 4
Unsupervised Learning:Applications to Networks and Text Mining
2015 Method Lectures
Cs6502 ooad-cse-vst-au-unit-v dce
This document provides information about object-oriented analysis and design (OOAD) and object-oriented testing strategies. It discusses mapping design to code, state-independent and state-dependent objects, UML attribute presentation, relationships in UML diagrams, database models, testing, class testing techniques, integration testing strategies, GUI testing challenges, and test case design for OO software. Key points include the steps to map design to code, defining state-independent and state-dependent objects, relationships that can be shown in UML, goals of integrated testing, challenges of class and GUI testing, and techniques for test case design including state transition testing, use case scenario testing, and cluster testing.
ANP-GP Approach for Selection of Software Architecture Styles
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
Data collection requires evaluators to consider a wide and diverse v.docx
Data collection requires evaluators to consider a wide and diverse variety of data sources. These include both primary and secondary data sources. Data collection can significantly impact your evaluation project. Evaluators sometimes overlook problems where secondary data is involved and overlook the fact that data for comparison purposes can affect validity. The types of data in the Learning Resources are not meant to be exhaustive but are the most common types in program evaluation.
To prepare for this Discussion, choose one of the cases provided, review the specified Learning Resources, and complete the corresponding prompts.
Case 1 Steven Levitt, the author of Freakonomics, is exemplary in his ability to make data tell “stories.” Students of public administration and organizational behavior are familiar with the famous Hawthorne effect. Levitt and List’s (2009) reanalysis of the original Hawthorne study (which uses incomplete, archived, and secondary data) shows how management programs that were developed to increase worker productivity can be evaluated or reevaluated, even though the experiments were conducted almost a century ago. Post by Day 3 a description of at least two data collection problems that the reanalysis of Levitt and List might bring to the Hawthorne effect. Respond by Day 5 to two colleagues and explain whether the problems have to do with design or data collection. What other data collection problems could, in your view, also be important? Explain.
Case 2 Barbara Geddes (1990) points out the pitfalls of selecting cases, units, or observations purely on the basis of the dependent variable.
Post a brief explanation of the relevance of Barbara Geddes’ argument for program evaluation. Then, explain when a scenario of choosing cases solely on the dependent variable might be permissible. Provide a rationale for your explanations.
•Langbein, L. (2012). Public program evaluation: A statistical guide (2nd ed.). Armonk, NY: ME Sharpe. ◦Chapter 7, “Designing Useful Surveys for Evaluation” (pp. 209–238)
•McDavid, J. C., Huse, I., & Hawthorn, L. R. L. (2013). Program evaluation and performance measurement: An introduction to practice (2nd ed.). Thousand Oaks, CA: Sage. ◦Chapter 4, “Measurement for Program Evaluation and Performance Monitoring” (pp. 145–185)
•Geddes, B. (1990). How the cases you choose affect the answers you get: Selection bias in comparative politics. Political Analysis, 2(1), 131–150. Retrieved from http://www.uky.edu/~clthyn2/PS671/Geddes_1990PA.pdf
•Levitt, S., & List, J. (2009). Was there really a Hawthorne effect at the Hawthorne plant? An analysis of the original illumination experiments. Retrieved from http://www.nber.org/papers/w15016.pdf
•Urban Institute. (2014). Outcome indicators project. Retrieved from http://www.urban.org/center/cnp/projects/outcomeindicators.cfm
•Bamberger, M. (2010). Reconstructuring baseline data for impact evaluation and results measurement. Re.
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영
검색 및 추천 시스템의 사회적 역할이 커지면서, 그 결과의 공정성 역시 최근 관심사로 대두되었다. 본 발표에서는 검색 및 추천시스템의 공정성 이슈 및 그 해법을 다룬다. 공정한 검색 및 추천 결과를 정의하는 다양한 방법, 공정성의 결여가 미치는 자원 배분 및 스테레오타이핑 문제, 그리고 검색 및 추천시스템 개발의 각 단계별로 어떤 해결책이 있는지를 최신 연구 중심으로 살펴본다. 마지막으로 실제 공정한 시스템 개발을 위한 실무적인 고려사항을 다룬다.
677-Session 11-Data Analysis-S12
Spring 2012
Dr. Diane Nahl
LIS 677 Human Dimension in Information System University of Hawaii
LIS Program
acmsigtalkshare-121023190142-phpapp01.pptx
Netflix uses a variety of techniques to provide personalized recommendations to users. Some key aspects include:
1. Netflix recommendations are generated using both offline and online techniques. Offline techniques allow for more complex computations but results may become stale, while online techniques can respond quickly but have stricter time constraints.
2. Recommendations are generated using a variety of data sources and machine learning models, including SVD, RBMs, gradient boosted trees, and other techniques. Both the data and models are important for generating high quality recommendations.
3. Netflix tests recommendations using both offline and online A/B testing techniques. Offline testing is used to evaluate new models and ideas before launching online tests involving real users
Continuous Improvement: How systems design can benefit the data-driven design...
This document discusses how systems design can support data-driven continuous improvement in K-12 education. It is a complex technical system with many interrelated parts. Data-driven decision making has shown promise in addressing achievement gaps but requires defining goals, measures, and actions. Systems design principles like defining measurable goals, mapping the system, and incorporating feedback loops can help educational organizations implement continuous improvement. However, goals and metrics must be carefully aligned to avoid unintended consequences from data-driven processes. Overall systems design may help scale data use to support small, ongoing improvements across the education system.
Empirical Evaluation of Active Learning in Recommender Systems
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor quality data during training. Active learning aims to remedy this problem by focusing on obtaining better quality data that more aptly reflects a user’s preferences. In attempt to do that, an active learning strategy selects the best items to be presented to the user in order to acquire her ratings and hence improve the output of the RS.
In this seminar, I present a set of active learning strategies with different characteristics and the evaluation results with respect to several evaluation measures (i.e., MAE, NDCG, Precision, Coverage, Recommendation Quality, and, Quantity of the acquired ratings and contextual conditions).
The traditional evaluation of active learning strategies has two major flaws: (1) Performance has been evaluated for each user independently (ignoring system-wide improvements) (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). Addressing these flaws, I present that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system-centric). Hence, I present a novel offline evaluation methodology and use it to evaluate some novel and state of the art rating elicitation strategies.
While the first set of experiments was done offline, the true value of active learning must be evaluated in an online setting. Hence, in the second part of the seminar, I present a novel active learning approach that exploits some additional information of the user (i.e. the user’s personality) to deal with the cold start problem in an up-and-running mobile context-aware RS called STS, that provides users with recommendations for places of interest (POIs). The results of live user studies, have shown that the proposed AL approach significantly increases the quantity of the ratings and contextual conditions acquired from the user as well as the recommendation accuracy.
Similarity learning
This presentation educate you through Similarity Learning, Goal of Similarity Learning, The Learning Algorithm, Experimental Setting, Quality Measures and Conclusions of Similarity Learning.
For more topics stay tuned with Learnbay.
Toward a Recommendation System for focusing Testing
1) The document proposes a recommendation system to identify critical classes in object-oriented software projects to focus testing efforts.
2) It uses a combination of ClassRank, which applies PageRank to class diagrams to measure importance, and Evolution Cost, which quantifies changes to a class and its relations over time.
3) The approach was tested on the Mozilla software project and identified classes that were frequently changed and highly connected to other classes.
Promise 2011: "Local Bias and its Impacts on the Performance of Parametric Es...
This document discusses local bias in parametric estimation models and its impact on model performance. It defines local bias as the deviation between parameters calibrated from local data versus general model defaults. An analysis of a software cost estimation model finds local bias varies between data groups and is positively correlated with decreased model accuracy and increased uncertainty, as measured by mean and variance of magnitude of relative error. The implications are that local bias should be identified and addressed to improve model evolution and balance accuracy versus stability.
Ranking Related News Predictions
We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
Yong Zheng. "Deviation-Based and Similarity-Based Contextual SLIM Recommendation Algorithms". ACM RecSys Doctoral Symposium, Proceedings of the 8th ACM Conference on Recommender Systems (ACM RecSys 2014), pp. 437-440, Silicon Valley, CA, USA, Oct 2014 [Doctoral Symposium, Acceptance rate: 47%]
LAK13 linkedup tutorial_evaluation_framework
Tutorial given at LAK13 conference, Leuven, April, 9th, 2013. The presentation is informed by WP2 of the LinkedUp-project.eu that develops an Evaluation Framework for Open Web Data (Linked Data) Applications for Education purposes.
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Developing an effective AI/ML model risk management program, with topics covering
- Understanding machine learning lifecycle in banking
- Understanding key elements of machine learning model validation
- Testing modules for conceptual soundness
- Testing modules for outcome analysis
- Developing inherently interpretable benchmark models
- Developing the automated pipeline for streamlined validation
- Enabling automated validation and monitoring for dynamically updating models
data_mining_Projectreport
This document summarizes a research project that used a naive Bayes classifier to analyze comments on an Optional Practical Training program. The researchers collected over 42,000 comments from an online Department of Homeland Security forum and labeled 900 for training and testing a multinomial naive Bayes model. They achieved 96.5% accuracy by further enhancing the model using an iterative classification maximization algorithm. The results found that 85.17% of comments supported the OPT extension while 14.83% opposed it. Additional analysis on ethnicity found Chinese commenters more supportive than Americans.
Social Science Statistics STA2122.501 ● ONLINE Project 3
This document provides instructions and materials for a project comparing social values and attitudes between countries using data from the World Values Survey. Students are asked to select two variables - one for grouping countries and one for a social value - perform descriptive statistics and a t-test, and write a report relating their findings to a UN Sustainable Development Goal. Options for analyses comparing views of competition between Japan and the US, views of domestic violence between Sweden and the US, and views of technology between China and the US are provided.
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
原版一模一样【微信：741003700 】【(lbs毕业证书)伦敦商学院毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
More Related Content
Similar to ICML UDL Evaluating Deep Learning Models Applications to NLP Nazneen Rajani.pdf
Journal club: Meta-Prod2Vec
This document discusses Meta-Prod2Vec, a method for product recommendation that uses product embeddings learned from both co-purchase data and side information (metadata). It extends Prod2Vec, which learns embeddings from co-purchase data alone. Meta-Prod2Vec uses a loss function that incorporates both embedding similarities and metadata similarities to generate recommendations. The experiments show Meta-Prod2Vec improves recommendations for cold start products and performs best when combined with traditional collaborative filtering in an ensemble model.
Landscape of AI/ML in 2023
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Recommenders, Topics, and Text
Recommenders, Topics, and Text
Lecture 4
Unsupervised Learning:Applications to Networks and Text Mining
2015 Method Lectures
Cs6502 ooad-cse-vst-au-unit-v dce
This document provides information about object-oriented analysis and design (OOAD) and object-oriented testing strategies. It discusses mapping design to code, state-independent and state-dependent objects, UML attribute presentation, relationships in UML diagrams, database models, testing, class testing techniques, integration testing strategies, GUI testing challenges, and test case design for OO software. Key points include the steps to map design to code, defining state-independent and state-dependent objects, relationships that can be shown in UML, goals of integrated testing, challenges of class and GUI testing, and techniques for test case design including state transition testing, use case scenario testing, and cluster testing.
ANP-GP Approach for Selection of Software Architecture Styles
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
Data collection requires evaluators to consider a wide and diverse v.docx
Data collection requires evaluators to consider a wide and diverse variety of data sources. These include both primary and secondary data sources. Data collection can significantly impact your evaluation project. Evaluators sometimes overlook problems where secondary data is involved and overlook the fact that data for comparison purposes can affect validity. The types of data in the Learning Resources are not meant to be exhaustive but are the most common types in program evaluation.
To prepare for this Discussion, choose one of the cases provided, review the specified Learning Resources, and complete the corresponding prompts.
Case 1 Steven Levitt, the author of Freakonomics, is exemplary in his ability to make data tell “stories.” Students of public administration and organizational behavior are familiar with the famous Hawthorne effect. Levitt and List’s (2009) reanalysis of the original Hawthorne study (which uses incomplete, archived, and secondary data) shows how management programs that were developed to increase worker productivity can be evaluated or reevaluated, even though the experiments were conducted almost a century ago. Post by Day 3 a description of at least two data collection problems that the reanalysis of Levitt and List might bring to the Hawthorne effect. Respond by Day 5 to two colleagues and explain whether the problems have to do with design or data collection. What other data collection problems could, in your view, also be important? Explain.
Case 2 Barbara Geddes (1990) points out the pitfalls of selecting cases, units, or observations purely on the basis of the dependent variable.
Post a brief explanation of the relevance of Barbara Geddes’ argument for program evaluation. Then, explain when a scenario of choosing cases solely on the dependent variable might be permissible. Provide a rationale for your explanations.
•Langbein, L. (2012). Public program evaluation: A statistical guide (2nd ed.). Armonk, NY: ME Sharpe. ◦Chapter 7, “Designing Useful Surveys for Evaluation” (pp. 209–238)
•McDavid, J. C., Huse, I., & Hawthorn, L. R. L. (2013). Program evaluation and performance measurement: An introduction to practice (2nd ed.). Thousand Oaks, CA: Sage. ◦Chapter 4, “Measurement for Program Evaluation and Performance Monitoring” (pp. 145–185)
•Geddes, B. (1990). How the cases you choose affect the answers you get: Selection bias in comparative politics. Political Analysis, 2(1), 131–150. Retrieved from http://www.uky.edu/~clthyn2/PS671/Geddes_1990PA.pdf
•Levitt, S., & List, J. (2009). Was there really a Hawthorne effect at the Hawthorne plant? An analysis of the original illumination experiments. Retrieved from http://www.nber.org/papers/w15016.pdf
•Urban Institute. (2014). Outcome indicators project. Retrieved from http://www.urban.org/center/cnp/projects/outcomeindicators.cfm
•Bamberger, M. (2010). Reconstructuring baseline data for impact evaluation and results measurement. Re.
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영
검색 및 추천 시스템의 사회적 역할이 커지면서, 그 결과의 공정성 역시 최근 관심사로 대두되었다. 본 발표에서는 검색 및 추천시스템의 공정성 이슈 및 그 해법을 다룬다. 공정한 검색 및 추천 결과를 정의하는 다양한 방법, 공정성의 결여가 미치는 자원 배분 및 스테레오타이핑 문제, 그리고 검색 및 추천시스템 개발의 각 단계별로 어떤 해결책이 있는지를 최신 연구 중심으로 살펴본다. 마지막으로 실제 공정한 시스템 개발을 위한 실무적인 고려사항을 다룬다.
677-Session 11-Data Analysis-S12
Spring 2012
Dr. Diane Nahl
LIS 677 Human Dimension in Information System University of Hawaii
LIS Program
acmsigtalkshare-121023190142-phpapp01.pptx
Netflix uses a variety of techniques to provide personalized recommendations to users. Some key aspects include:
1. Netflix recommendations are generated using both offline and online techniques. Offline techniques allow for more complex computations but results may become stale, while online techniques can respond quickly but have stricter time constraints.
2. Recommendations are generated using a variety of data sources and machine learning models, including SVD, RBMs, gradient boosted trees, and other techniques. Both the data and models are important for generating high quality recommendations.
3. Netflix tests recommendations using both offline and online A/B testing techniques. Offline testing is used to evaluate new models and ideas before launching online tests involving real users
Continuous Improvement: How systems design can benefit the data-driven design...
This document discusses how systems design can support data-driven continuous improvement in K-12 education. It is a complex technical system with many interrelated parts. Data-driven decision making has shown promise in addressing achievement gaps but requires defining goals, measures, and actions. Systems design principles like defining measurable goals, mapping the system, and incorporating feedback loops can help educational organizations implement continuous improvement. However, goals and metrics must be carefully aligned to avoid unintended consequences from data-driven processes. Overall systems design may help scale data use to support small, ongoing improvements across the education system.
Empirical Evaluation of Active Learning in Recommender Systems
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor quality data during training. Active learning aims to remedy this problem by focusing on obtaining better quality data that more aptly reflects a user’s preferences. In attempt to do that, an active learning strategy selects the best items to be presented to the user in order to acquire her ratings and hence improve the output of the RS.
In this seminar, I present a set of active learning strategies with different characteristics and the evaluation results with respect to several evaluation measures (i.e., MAE, NDCG, Precision, Coverage, Recommendation Quality, and, Quantity of the acquired ratings and contextual conditions).
The traditional evaluation of active learning strategies has two major flaws: (1) Performance has been evaluated for each user independently (ignoring system-wide improvements) (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). Addressing these flaws, I present that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system-centric). Hence, I present a novel offline evaluation methodology and use it to evaluate some novel and state of the art rating elicitation strategies.
While the first set of experiments was done offline, the true value of active learning must be evaluated in an online setting. Hence, in the second part of the seminar, I present a novel active learning approach that exploits some additional information of the user (i.e. the user’s personality) to deal with the cold start problem in an up-and-running mobile context-aware RS called STS, that provides users with recommendations for places of interest (POIs). The results of live user studies, have shown that the proposed AL approach significantly increases the quantity of the ratings and contextual conditions acquired from the user as well as the recommendation accuracy.
Similarity learning
This presentation educate you through Similarity Learning, Goal of Similarity Learning, The Learning Algorithm, Experimental Setting, Quality Measures and Conclusions of Similarity Learning.
For more topics stay tuned with Learnbay.
Toward a Recommendation System for focusing Testing
1) The document proposes a recommendation system to identify critical classes in object-oriented software projects to focus testing efforts.
2) It uses a combination of ClassRank, which applies PageRank to class diagrams to measure importance, and Evolution Cost, which quantifies changes to a class and its relations over time.
3) The approach was tested on the Mozilla software project and identified classes that were frequently changed and highly connected to other classes.
Promise 2011: "Local Bias and its Impacts on the Performance of Parametric Es...
This document discusses local bias in parametric estimation models and its impact on model performance. It defines local bias as the deviation between parameters calibrated from local data versus general model defaults. An analysis of a software cost estimation model finds local bias varies between data groups and is positively correlated with decreased model accuracy and increased uncertainty, as measured by mean and variance of magnitude of relative error. The implications are that local bias should be identified and addressed to improve model evolution and balance accuracy versus stability.
Ranking Related News Predictions
We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
Yong Zheng. "Deviation-Based and Similarity-Based Contextual SLIM Recommendation Algorithms". ACM RecSys Doctoral Symposium, Proceedings of the 8th ACM Conference on Recommender Systems (ACM RecSys 2014), pp. 437-440, Silicon Valley, CA, USA, Oct 2014 [Doctoral Symposium, Acceptance rate: 47%]
LAK13 linkedup tutorial_evaluation_framework
Tutorial given at LAK13 conference, Leuven, April, 9th, 2013. The presentation is informed by WP2 of the LinkedUp-project.eu that develops an Evaluation Framework for Open Web Data (Linked Data) Applications for Education purposes.
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Developing an effective AI/ML model risk management program, with topics covering
- Understanding machine learning lifecycle in banking
- Understanding key elements of machine learning model validation
- Testing modules for conceptual soundness
- Testing modules for outcome analysis
- Developing inherently interpretable benchmark models
- Developing the automated pipeline for streamlined validation
- Enabling automated validation and monitoring for dynamically updating models
data_mining_Projectreport
This document summarizes a research project that used a naive Bayes classifier to analyze comments on an Optional Practical Training program. The researchers collected over 42,000 comments from an online Department of Homeland Security forum and labeled 900 for training and testing a multinomial naive Bayes model. They achieved 96.5% accuracy by further enhancing the model using an iterative classification maximization algorithm. The results found that 85.17% of comments supported the OPT extension while 14.83% opposed it. Additional analysis on ethnicity found Chinese commenters more supportive than Americans.
Social Science Statistics STA2122.501 ● ONLINE Project 3
This document provides instructions and materials for a project comparing social values and attitudes between countries using data from the World Values Survey. Students are asked to select two variables - one for grouping countries and one for a social value - perform descriptive statistics and a t-test, and write a report relating their findings to a UN Sustainable Development Goal. Options for analyses comparing views of competition between Japan and the US, views of domestic violence between Sweden and the US, and views of technology between China and the US are provided.
Similar to ICML UDL Evaluating Deep Learning Models Applications to NLP Nazneen Rajani.pdf (20)
ANP-GP Approach for Selection of Software Architecture Styles
ANP-GP Approach for Selection of Software Architecture Styles
Data collection requires evaluators to consider a wide and diverse v.docx
Data collection requires evaluators to consider a wide and diverse v.docx
Continuous Improvement: How systems design can benefit the data-driven design...
Continuous Improvement: How systems design can benefit the data-driven design...
Empirical Evaluation of Active Learning in Recommender Systems
Empirical Evaluation of Active Learning in Recommender Systems
Toward a Recommendation System for focusing Testing
Toward a Recommendation System for focusing Testing
Promise 2011: "Local Bias and its Impacts on the Performance of Parametric Es...
Promise 2011: "Local Bias and its Impacts on the Performance of Parametric Es...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Social Science Statistics STA2122.501 ● ONLINE Project 3
Social Science Statistics STA2122.501 ● ONLINE Project 3
Recently uploaded
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
原版一模一样【微信：741003700 】【(lbs毕业证书)伦敦商学院毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(lbs毕业证书)伦敦商学院毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
一比一原版(UO毕业证)渥太华大学毕业证如何办理
原件一模一样【微信：95270640】【渥太华大学毕业证UO学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证UO学位证毕业证offer【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Jio cinema Retention & Engagement Strategy.pdf
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版英国赫特福德大学毕业证（hertfordshire毕业证书）如何办理
原版一模一样【微信：741003700 】【英国赫特福德大学毕业证（hertfordshire毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
原版办理【微信号:BYZS866】【南十字星大学毕业证(SCU毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
原版制作【微信号:BYZS866】【格里菲斯大学毕业证(Griffith毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
原版制作【微信:41543339】【(Sheffield毕业证书)谢菲尔德大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版澳洲西澳大学毕业证（uwa毕业证书）如何办理
原版一模一样【微信：741003700 】【澳洲西澳大学毕业证（uwa毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Recently uploaded (20)
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
ICML UDL Evaluating Deep Learning Models Applications to NLP Nazneen Rajani.pdf
- 1. Evaluating Deep Learning Models Applications to NLP Nazneen Rajani
- 2. Outline Part 1: Evaluation status quo Goal of evaluation is to inform next action: ● further analysis or ● model patching Robustness Gym (Goel et al., 2021) Part 2: Caveats with evaluating PLMs SummVis (Vig et al., 2021)
- 3. Outline Part 1: Evaluation status quo Goal of evaluation is to inform next action: ● further analysis or ● model patching Robustness Gym (Goel et al., 2021) Part 2: Caveats with evaluating PLMs SummVis (Vig et al., 2021)
- 4. ML Pipeline Collect data Train model Evaluate Deploy
- 5. Status Quo ML models seemingly perform well -- ● when data is iid ● evaluation measures aggregate performance ● but performance deteriorates on tail data ● and cannot generalize to ood data
- 6. Evaluation Landscape in NLP Aggregate evaluation BERT-base (Devlin et al.) model card
- 7. Evaluation Landscape in NLP Aggregate evaluation BERT-base (Devlin et al.) model card Adversarial evaluation Round 2 of ANLI (Nie et al.) Premise: Toolbox Murders is a 2004 horror film directed by Tobe Hooper, and written by Jace Anderson and Adam Gierasch. It is a remake of the 1978 film of the same name and was produced by the same people behind the original. The film centralizes on the occupants of an apartment who are stalked and murdered by a masked killer. Hypothesis: Toolbox Murders is both 41 years old and 15 years old. Gold label: Entailment Predicted label: Contradiction
- 8. Existing Evaluation Landscape Slew of work on evaluation in NLP -- tools and research papers
- 9. Goals of Evaluation Next action for user: 1. further evaluation/analysis, or 2. model patching for robustness
- 10. Robustness Gym Toolkit for unified evaluation and reporting Consolidated Reporting Fine-grained evaluations ● Subpopulations ● Transformations ● Evaluation Sets ● Attacks
- 11. RG iterative evaluation: 1. Contemplate
- 12. RG iterative evaluation: 2. Create
- 13. RG iterative evaluation: 3. Consolidate
- 14. RG iterative evaluation: 3Cs
- 15. How does RG support evaluation goals?
- 16. Example 1: Natural Language Inference Classify a pair of sentences as being in a relation of entailment, neutral, or contradiction Entailment Premise: If it were not for COVID, we would all be at the conference Hypothesis: We are not at the conference
- 17. Robustness Report for Natural Language Inference using bert-base-uncased on SNLI
- 18. Robustness Report for Natural Language Inference using bert-base-uncased on SNLI
- 19. Robustness Report for Natural Language Inference using bert-base-uncased on SNLI
- 20. Next action: further analysis Hypothesis: Possible spurious correlation between negation and contradiction class Action: Evaluate on counterfactually augmented eval sets Observation: large performance drops on class-balanced dataset
- 21. Example 2: Named Entity Linking Map mentions of entities to entries in a KB like the Wikipedia FIFA World Cup England National Football Team When did England last win the football world cup?
- 22. Evaluate NEL models using RG Create subpopulations of interest: ● Popular entities ● Tail entities ● Topics such as soccer, winter sports, etc. Evaluate models: ● Academic: Bootleg (Orr et al., ‘20), REL (Hulst et al., ‘20) ● Commercial: Microsoft, Google, Amazon ● Heuristic: Popular Dataset: AIDA (Hoffart et al., 2011)
- 23. Results on the AIDA-b dataset Popularity heuristic outperforms all commercial systems F1
- 24. Results on the AIDA-b dataset Commercial systems are capitalization sensitive
- 25. Results on the AIDA-b dataset Bootleg is robust across sports F1
- 26. Next action: model patching Assuming sports application downstream Action: Patch model using weak labeling (Goel et al., NAACL ‘21 industry tack) best off-the-shelf system
- 27. Next action: model patching Assuming sports application downstream Action: Patch model using weak labeling (Goel et al., NAACL ‘21 industry tack) best off-the-shelf system fix poor performance
- 28. Next action: model patching Assuming sports application downstream Action: Patch model using weak labeling (Goel et al., NAACL ‘21 industry tack) Observation: 25% improvement in sports-related errors best off-the-shelf system fix poor performance
- 29. ML iterative pipeline Collect data Train model Evaluate Deploy Model patching Analysis
- 30. Goldilocks spectrum for Evaluation Aggregate evaluations Adversarial attacks Subpopulations Distribution shift Transformations Diagnostic sets
- 31. Outline Part 1: Evaluation status quo Goal of evaluation is to inform next action: ● further analysis or ● model patching Robustness Gym (Goel et al., 2021) Part 2: Caveats with evaluating PLMs SummVis (Vig et al., 2021)
- 32. Caveats with evaluating PLMs Input contamination ● Overlap between pre-training and evaluation data ● Reasons: ○ Some task datasets are crawled from the web, eg., news summarization ○ Datasets (with or without labels) are uploaded to the web
- 33. SummVis Toolkit for interactive visual analysis for text summarization Consolidated View Multi-dimensional fine-grained analysis
- 36. Caveats with evaluating PLMs Input contamination is a problem Other works have also identified the problem of contamination (Brown et al., ‘20, Dodge et al., ‘21)
- 37. Caveats with evaluating PLMs Input contamination is a problem Other works have also identified the problem of contamination (Brown et al., ‘20, Dodge et al., ‘21) How do we evaluate models with known or possible input contamination?
- 38. Takeaways Goal of evaluation is to inform next action Evaluation is an iterative process Disaggregation helps expose model vulnerabilities Challenges associated with evaluating PLMs can obscure model vulnerabilities Other aspects not discussed: Evaluation metrics (GEM by Gehrmann et al., ‘21, GENIE by Khashabi et al., ‘21) Evaluation datasets/ task design (Rogers, ‘21, Bowman and Dahl ‘21)
- 39. Thank you for listening Papers: 1. Robustness Gym: Unifying the NLP Evaluation Landscape (NAACL ‘21 demo) 2. Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems (NAACL ‘21 industry track) 3. SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (ACL ‘21 demo) Collaborators: Jesse Vig (Salesforce) Karan Goel (Stanford) Chris Ré (Stanford) Mohit Bansal (UNC) Wojciech Kryscinski (Salesforce) Silvio Savarese (Salesforce)