Emily Denton - Unsupervised Learning of Disentangled Representations from Vid...Luba Elliott
This talk by Emily Denton from New York University on "Unsupervised Learning of Disentangled Representations from Video" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
InfoGAN: Interpretable Representation Learning by Information Maximizing Gene...홍배 김
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
오사카 대학 박사과정인 Takato Horii군이 작성한 자료
데이터 생성 모델로 우수한 GAN을 이용하여 비지도학습을 통해
"알기쉬게" 이미지의 정보를 표현하는 특징량을 "간단하게"획득하기
* 특징이 서로 얽혀있는 Physical space에서 서로 독립적인 Eigen space로 변환하는 것과 같은 원리
Emily Denton - Unsupervised Learning of Disentangled Representations from Vid...Luba Elliott
This talk by Emily Denton from New York University on "Unsupervised Learning of Disentangled Representations from Video" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
InfoGAN: Interpretable Representation Learning by Information Maximizing Gene...홍배 김
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
오사카 대학 박사과정인 Takato Horii군이 작성한 자료
데이터 생성 모델로 우수한 GAN을 이용하여 비지도학습을 통해
"알기쉬게" 이미지의 정보를 표현하는 특징량을 "간단하게"획득하기
* 특징이 서로 얽혀있는 Physical space에서 서로 독립적인 Eigen space로 변환하는 것과 같은 원리
PR-409: Denoising Diffusion Probabilistic ModelsHyeongmin Lee
이번 논문은 요즘 핫한 Diffusion을 처음으로 유행시킨 Denoising Diffusion Probabilistic Models (DDPM) 입니다. ICML 2015년에 처음 제안된 Diffusion의 여러 실용적인 측면들을 멋지게 해결하여 그 유행의 시작을 알린 논문인데요, Generative Model의 여러 분야와 Diffusion, 그리고 DDPM에서는 무엇이 바뀌었는지 알아보도록 하겠습니다.
논문 링크: https://arxiv.org/abs/2006.11239
영상 링크: https://youtu.be/1j0W_lu55nc
An overview of gradient descent optimization algorithms Hakky St
勾配降下法についての論文をスライドにしたものです。
This is the slide for study meeting of gradient descent.
I use this paper and this is very good information about gradient descent.
https://arxiv.org/abs/1609.04747
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/fotonation/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-corcoran-tuesday
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Peter Corcoran, co-founder of FotoNation (now a core business unit of Xperi) and lead principle investigator and director of C3Imaging (a research partnership between Xperi and the National University of Ireland, Galway), presents the "Getting More from Your Datasets: Data Augmentation, Annotation and Generative Techniques" tutorial at the May 2018 Embedded Vision Summit.
Deep learning for embedded vision requires large datasets. Indeed, the more varied the training data is, the more accurate the resultant trained network tends to also be. But, acquiring and accurately annotating datasets costs time and money. This talk shows how to get more out of existing datasets.
First, state-of-art data augmentation techniques are reviewed, and a new approach, smart augmentation, is explained. Next, GANs (generative adversarial networks) that learn the structure of an existing dataset are explained; several example use cases (such as creating a very large dataset of facial training data) show how GANs can generate new data corresponding to the original dataset.
But building a dataset does not by itself represent the entirety of the challenge; data must also be annotated in a way that is meaningful for the training process. The presentation then gives an example of training a GAN from a dataset that incorporates annotations. This technique enables the generation of pre-annotated data" providing an exciting way to create large datasets at significantly reduced costs.
DataEngConf: Feature Extraction: Modern Questions and Challenges at GoogleHakka Labs
By Dmitry Storcheus (Engineer, Google Research)
Feature extraction, as usually understood, seeks an optimal transformation from raw data into features that can be used as an input for a learning algorithm. In recent times this problem has been attacked using a growing number of diverse techniques that originated in separate research communities: from PCA and LDA to manifold and metric learning. The goal of this talk is to contrast and compare feature extraction techniques coming from different machine learning areas as well as discuss the modern challenges and open problems in feature extraction. Moreover, this talk will suggest novel solutions to some of the challenges discussed, particularly to coupled feature extraction.
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
Slides for "Do Deep Generative Models Know What They Don't know?"Julius Hietala
My slides that discuss different deep generative models, mainly normalizing flows for density estimation at a deep learning seminar at Aalto University fall 2019.
PR-409: Denoising Diffusion Probabilistic ModelsHyeongmin Lee
이번 논문은 요즘 핫한 Diffusion을 처음으로 유행시킨 Denoising Diffusion Probabilistic Models (DDPM) 입니다. ICML 2015년에 처음 제안된 Diffusion의 여러 실용적인 측면들을 멋지게 해결하여 그 유행의 시작을 알린 논문인데요, Generative Model의 여러 분야와 Diffusion, 그리고 DDPM에서는 무엇이 바뀌었는지 알아보도록 하겠습니다.
논문 링크: https://arxiv.org/abs/2006.11239
영상 링크: https://youtu.be/1j0W_lu55nc
An overview of gradient descent optimization algorithms Hakky St
勾配降下法についての論文をスライドにしたものです。
This is the slide for study meeting of gradient descent.
I use this paper and this is very good information about gradient descent.
https://arxiv.org/abs/1609.04747
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/fotonation/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-corcoran-tuesday
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Peter Corcoran, co-founder of FotoNation (now a core business unit of Xperi) and lead principle investigator and director of C3Imaging (a research partnership between Xperi and the National University of Ireland, Galway), presents the "Getting More from Your Datasets: Data Augmentation, Annotation and Generative Techniques" tutorial at the May 2018 Embedded Vision Summit.
Deep learning for embedded vision requires large datasets. Indeed, the more varied the training data is, the more accurate the resultant trained network tends to also be. But, acquiring and accurately annotating datasets costs time and money. This talk shows how to get more out of existing datasets.
First, state-of-art data augmentation techniques are reviewed, and a new approach, smart augmentation, is explained. Next, GANs (generative adversarial networks) that learn the structure of an existing dataset are explained; several example use cases (such as creating a very large dataset of facial training data) show how GANs can generate new data corresponding to the original dataset.
But building a dataset does not by itself represent the entirety of the challenge; data must also be annotated in a way that is meaningful for the training process. The presentation then gives an example of training a GAN from a dataset that incorporates annotations. This technique enables the generation of pre-annotated data" providing an exciting way to create large datasets at significantly reduced costs.
DataEngConf: Feature Extraction: Modern Questions and Challenges at GoogleHakka Labs
By Dmitry Storcheus (Engineer, Google Research)
Feature extraction, as usually understood, seeks an optimal transformation from raw data into features that can be used as an input for a learning algorithm. In recent times this problem has been attacked using a growing number of diverse techniques that originated in separate research communities: from PCA and LDA to manifold and metric learning. The goal of this talk is to contrast and compare feature extraction techniques coming from different machine learning areas as well as discuss the modern challenges and open problems in feature extraction. Moreover, this talk will suggest novel solutions to some of the challenges discussed, particularly to coupled feature extraction.
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
Slides for "Do Deep Generative Models Know What They Don't know?"Julius Hietala
My slides that discuss different deep generative models, mainly normalizing flows for density estimation at a deep learning seminar at Aalto University fall 2019.
Machine learning workshop, session 3.
- Data sets
- Machine Learning Algorithms
- Algorithms by Learning Style
- Algorithms by Similarity
- People to follow
Joint contrastive learning with infinite possibilitiestaeseon ryu
Contrastive Learning은 두 이미지가 유사한지 유사하지 않은 지에 대해서 어떤 label이 없이 피쳐들을 배우게 하는 머신 learning 테크닉 중에 하나입니다 우리는 기존에 있는 Supervised learning과 조금 차이가 있는데 Supervised learning은 label cost가 들고
그다음에 Task specific 하기 때문에 generalizability가 조금 떨어질 수 있습니다 하지만 Contrastive Learning은 label이 없이 진행하기때문에 label cost가 없고 generalizability가 조금 더 좋을수 있습니다. 해당 논문은 보다 유용한 Contrastive Learning을 위한 Joint Contrastive Learning에 대해 제안을 하는대요 https://youtu.be/0NLq-ikBP1I
PR-231: A Simple Framework for Contrastive Learning of Visual RepresentationsJinwon Lee
TensorFlow Korea 논문읽기모임 PR12 231번째 논문 review 입니다
이번 논문은 Google Brain에서 나온 A Simple Framework for Contrastive Learning of Visual Representations입니다. Geoffrey Hinton님이 마지막 저자이시기도 해서 최근에 더 주목을 받고 있는 논문입니다.
이 논문은 최근에 굉장히 핫한 topic인 contrastive learning을 이용한 self-supervised learning쪽 논문으로 supervised learning으로 학습한 ResNet50와 동일한 성능을 얻을 수 있는 unsupervised pre-trainig 방법을 제안하였습니다. Data augmentation, Non-linear projection head, large batch size, longer training, NTXent loss 등을 활용하여 훌륭한 representation learning이 가능함을 보여주었고, semi-supervised learning이나 transfer learning에서도 매우 뛰어난 결과를 보여주었습니다. 자세한 내용은 영상을 참고해주세요
논문링크: https://arxiv.org/abs/2002.05709
영상링크: https://youtu.be/FWhM3juUM6s
May 2015 talk to SW Data Meetup by Professor Hendrik Blockeel from KU Leuven & Leiden University.
With increasing amounts of ever more complex forms of digital data becoming available, the methods for analyzing these data have also become more diverse and sophisticated. With this comes an increased risk of incorrect use of these methods, and a greater burden on the user to be knowledgeable about their assumptions. In addition, the user needs to know about a wide variety of methods to be able to apply the most suitable one to a particular problem. This combination of broad and deep knowledge is not sustainable.
The idea behind declarative data analysis is that the burden of choosing the right statistical methodology for answering a research question should no longer lie with the user, but with the system. The user should be able to simply describe the problem, formulate a question, and let the system take it from there. To achieve this, we need to find answers to questions such as: what languages are suitable for formulating these questions, and what execution mechanisms can we develop for them? In this talk, I will discuss recent and ongoing research in this direction. The talk will touch upon query languages for data mining and for statistical inference, declarative modeling for data mining, meta-learning, and constraint-based data mining. What connects these research threads is that they all strive to put intelligence about data analysis into the system, instead of assuming it resides in the user.
Hendrik Blockeel is a professor of computer science at KU Leuven, Belgium, and part-time associate professor at Leiden University, The Netherlands. His research interests lie mostly in machine learning and data mining. He has made a variety of research contributions in these fields, including work on decision tree learning, inductive logic programming, predictive clustering, probabilistic-logical models, inductive databases, constraint-based data mining, and declarative data analysis. He is an action editor for Machine Learning and serves on the editorial board of several other journals. He has chaired or organized multiple conferences, workshops, and summer schools, including ILP, ECMLPKDD, IDA and ACAI, and he has been vice-chair, area chair, or senior PC member for ECAI, IJCAI, ICML, KDD, ICDM. He was a member of the board of the European Coordinating Committee for Artificial Intelligence from 2004 to 2010, and currently serves as publications chair for the ECMLPKDD steering committee.
A new CPXR Based Logistic Regression Method and Clinical Prognostic Modeling ...Vahid Taslimitehrani
Presented at 15th International Conference on BioInformatics and BioEngineering (BIBE2014)
Prognostic modeling is central to medicine, as it is often used to predict patients’ outcome and response to treatments and to identify important medical risk factors. Logistic regression is one of the most used approaches for clinical pre- diction modeling. Traumatic brain injury (TBI) is an important public health issue and a leading cause of death and disability worldwide. In this study, we adapt CPXR (Contrast Pattern Aided Regression, a recently introduced regression method), to develop a new logistic regression method called CPXR(Log), for general binary outcome prediction (including prognostic modeling), and we use the method to carry out prognostic modeling for TBI using admission time data. The models produced by CPXR(Log) achieved AUC as high as 0.93 and specificity as high as 0.97, much better than those reported by previous studies. Our method produced interpretable prediction models for diverse patient groups for TBI, which show that different kinds of patients should be evaluated differently for TBI outcome prediction and the odds ratios of some predictor variables differ significantly from those given by previous studies; such results can be valuable to physicians.
Using Feature Grouping as a Stochastic Regularizer for High Dimensional Noisy...WiMLDSMontreal
"Using Feature Grouping as a Stochastic Regularizer for High Dimensional Noisy Data"
By Sergül Aydöre, Assistant Professor at Stevens Institute of Technology
Abstract:
The use of complex models –with many parameters– is challenging with high-dimensional small-sample
problems: indeed, they face rapid overfitting. Such situations are common when data collection is expensive,
as in neuroscience, biology, or geology. Dedicated regularization can be crafted to tame overfit, typically via
structured penalties. But rich penalties require mathematical expertise and entail large computational costs.
Stochastic regularizers such as dropout are easier to implement: they prevent overfitting by random perturbations.
Used inside a stochastic optimizer, they come with little additional cost. We propose a structured stochastic
regularization that relies on feature grouping. Using a fast clustering algorithm, we define a family of
groups of features that capture feature covariations. We then randomly select these groups inside a stochastic
gradient descent loop. This procedure acts as a structured regularizer for high-dimensional correlated data
without additional computational cost and it has a denoising effect. We demonstrate the performance of our
approach for logistic regression both on a sample-limited face image dataset with varying additive noise and on
a typical high-dimensional learning problem, brain image classification.
Similar to Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (20)
Brief History of Visual Representation LearningSangwoo Mo
- [2012-2015] Evolution of deep learning architectures
- [2016-2019] Learning paradigms for diverse tasks
- [2020-current] Scaling laws and foundation models
Learning Visual Representations from Uncurated DataSangwoo Mo
Slide about the defense of my Ph.D. dissertation: "Learning Visual Representations from Uncurated Data"
It includes four papers about
- Learning from multi-object images for contrastive learning [1] and Vision Transformer (ViT) [2]
- Learning with limited labels (semi-sup) for image classification [3] and vision-language [4] models
[1] Mo*, Kang* et al. Object-aware Contrastive Learning for Debiased Scene Representation. NeurIPS’21.
[2] Kang*, Mo* et al. OAMixer: Object-aware Mixing Layer for Vision Transformers. CVPRW’22.
[3] Mo et al. RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data. ICLR’23.
[4] Mo et al. S-CLIP: Semi-supervised Vision-Language Pre-training using Few Specialist Captions. Under Review.
A Unified Framework for Computer Vision Tasks: (Conditional) Generative Model...Sangwoo Mo
Lab seminar introduces Ting Chen's recent 3 works:
- Pix2seq: A Language Modeling Framework for Object Detection (ICLR’22)
- A Unified Sequence Interface for Vision Tasks (NeurIPS’22)
- A Generalist Framework for Panoptic Segmentation of Images and Videos (submitted to ICLR’23)
Lab seminar on
- Sharpness-Aware Minimization for Efficiently Improving Generalization (ICLR 2021)
- When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations (under review)
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
1. Challenging Common Assumptions
in the Unsupervised Learning of
Disentangled Representations
(ICML 2019 Best Paper)
2019.07.17.
Sangwoo Mo
1
2. Outline
• Quick Review
• What is disentangled representation (DR)?
• Prior work on the unsupervised learning of DR
• Theoretical Results
• Unsupervised learning of DR is impossible without inductive biases
• Empirical Results
• Q1. Which method should be used?
• Q2. How to choose the hyperparameters?
• Q3. How to select the best model from a set of trained models?
2
3. Quick Review
• Disentangled representation: Learn a representation 𝑧 from the data 𝑥 s.t.
• Contain all the information of 𝑥 in a compact and interpretable structure
• Currently no single formal definition L (many definitions for the factor of variation)
3* Image from BetaVAE (ICLR 2017)
4. Quick Review: Prior Methods
• BetaVAE (ICLR 2017)
• Use 𝛽 > 1 for the VAE objective (force to the factorized Gaussian prior)
4
5. Quick Review: Prior Methods
• BetaVAE (ICLR 2017)
• Use 𝛽 > 1 for the VAE objective (force to the factorized Gaussian prior)
• FactorVAE (ICML 2018) & 𝜷-TCVAE (NeurIPS 2018)
• Penalize the total correlation of the representation, which is estimated1 by
adversarial learning (FactorVAE) or (biased) mini-batch approximation (𝛽-TCVAE)
51. It requires the aggregated posterior 𝑞(𝒛)
6. Quick Review: Prior Methods
• BetaVAE (ICLR 2017)
• Use 𝛽 > 1 for the VAE objective (force to the factorized Gaussian prior)
• FactorVAE (ICML 2018) & 𝜷-TCVAE (NeurIPS 2018)
• Penalize the total correlation of the representation, which is estimated1 by
adversarial learning (FactorVAE) or (biased) mini-batch approximation (𝛽-TCVAE)
• DIP-VAE (ICLR 2018)
• Match 𝑞(𝒛) to the disentangled prior 𝑝(𝒛), where 𝐷 is a (tractable) moment matching
61. It requires the aggregated posterior 𝑞(𝒛)
7. Quick Review: Evaluation Metrics
• Many heuristics are proposed to quantitatively evaluate the disentanglement
• Basic idea: Factors and representation should have 1-1 correspondence
7
8. Quick Review: Evaluation Metrics
• Many heuristics are proposed to quantitatively evaluate the disentanglement
• Basic idea: Factors and representation should have 1-1 correspondence
• BetaVAE (ICLR 2017) & FactorVAE (ICML 2018) metric
• Given a factor 𝑐., generate two (simulation) data 𝑥, 𝑥′ with same 𝑐. but different 𝑐1.,
then train a classifier to predict 𝑐. using the difference of the representation |𝑧 − 𝑧4|
• Indeed, the classifier will map the zero-valued index of |𝑧 − 𝑧4
| to the factor 𝑐.
8
9. Quick Review: Evaluation Metrics
• Many heuristics are proposed to quantitatively evaluate the disentanglement
• Basic idea: Factors and representation should have 1-1 correspondence
• BetaVAE (ICLR 2017) & FactorVAE (ICML 2018) metric
• Given a factor 𝑐., generate two (simulation) data 𝑥, 𝑥′ with same 𝑐. but different 𝑐1.,
then train a classifier to predict 𝑐. using the difference of the representation |𝑧 − 𝑧4|
• Indeed, the classifier will map the zero-valued index of |𝑧 − 𝑧4
| to the factor 𝑐.
• Mutual Information Gap (NeurIPS 2018)
• Compute the mutual information between each factor 𝑐. and each dimension of 𝑧5
• For the highest and second highest dimensions 𝑖7 and 𝑖8 of the mutual information,
measure the difference between them: 𝐼 𝑐., 𝑧5:
− 𝐼(𝑐., 𝑧5;
)
9
10. Theoretical Results
• “Unsupervised learning of disentangled representations is fundamentally impossible
without inductive biases on both the models and the data”
10
11. Theoretical Results
• “Unsupervised learning of disentangled representations is fundamentally impossible
without inductive biases on both the models and the data”
• Theorem. For 𝑝 𝒛 = ∏5>7
?
𝑝(𝑧5), there exists an infinite family of bijective functions 𝑓 s.t.
• 𝒛 and 𝑓(𝒛) are completely entangled (i.e.,
ABC(𝒖)
AEF
≠ 0 a.e. for all 𝑖, 𝑗)
• 𝒛 and 𝑓(𝒛) have same marginal distribution (i.e., 𝑃 𝒛 ≤ 𝒖 = 𝑃(𝑓 𝒛 ≤ 𝒖) for all 𝒖)
11
12. Theoretical Results
• “Unsupervised learning of disentangled representations is fundamentally impossible
without inductive biases on both the models and the data”
• Theorem. For 𝑝 𝒛 = ∏5>7
?
𝑝(𝑧5), there exists an infinite family of bijective functions 𝑓 s.t.
• 𝒛 and 𝑓(𝒛) are completely entangled (i.e.,
ABC(𝒖)
AEF
≠ 0 a.e. for all 𝑖, 𝑗)
• 𝒛 and 𝑓(𝒛) have same marginal distribution (i.e., 𝑃 𝒛 ≤ 𝒖 = 𝑃(𝑓 𝒛 ≤ 𝒖) for all 𝒖)
• Proof sketch. By construction.
• Let 𝑔: supp 𝒛 → 0,1 ?
s.t. 𝑔5 𝒗 = 𝑃(𝑧5 ≤ 𝑣5)
• Let ℎ: 0,1 ? → ℝ? s.t. ℎ5 𝒗 = 𝜓17(𝑣5) where 𝜓 is a c.d.f. of a normal distribution
• Then for any orthogonal matrix 𝑨, the following 𝑓 satisfies the condition:
𝑓 𝒖 = ℎ ∘ 𝑔 17(𝑨 ℎ ∘ 𝑔 𝒖 )
12
13. Theoretical Results
• “Unsupervised learning of disentangled representations is fundamentally impossible
without inductive biases on both the models and the data”
• Theorem. For 𝑝 𝒛 = ∏5>7
?
𝑝(𝑧5), there exists an infinite family of bijective functions 𝑓 s.t.
• 𝒛 and 𝑓(𝒛) are completely entangled (i.e.,
ABC(𝒖)
AEF
≠ 0 a.e. for all 𝑖, 𝑗)
• 𝒛 and 𝑓(𝒛) have same marginal distribution (i.e., 𝑃 𝒛 ≤ 𝒖 = 𝑃(𝑓 𝒛 ≤ 𝒖) for all 𝒖)
• Corollary. One cannot find the disentangled representation 𝑟(𝒙) (w.r.t. to the generative
model 𝐺(𝒙|𝒛)) as there are two equivalent generative models 𝐺 and 𝐺′ which has same
marginal distribution 𝑝(𝒙) but 𝒛4 = 𝑓(𝒛) is completely entangled w.r.t. 𝒛 (so as 𝑟(𝒙))
• Namely, inferring representation 𝒛 from observation 𝒙 is not a well-defined problem
13
14. Theoretical Results
• 𝛽-VAE learns some decorrelated features, but they are not semantically decomposed
• E.g., the width is entangled with the leg style in 𝛽-VAE
14* Image from BetaVAE (ICLR 2017)
15. Empirical Results
• Q1. Which method should be used?
• A. Hyperparameters and random seeds matter more than the choice of the model
15
16. Empirical Results
• Q2. How to choose the hyperparameters?
• A. Selecting the best hyperparameter is extremely hard due to the randomness
16
17. Empirical Results
• Q2. How to choose the hyperparameters?
• A. Also, there is no obvious trend over the variation of hyperparameters
17
18. Empirical Results
• Q2. How to choose the hyperparameters?
• A. Good hyperparameters often can be transferred (e.g., dSprites → color-dSprites)
18
Rank correlation matrix
19. Empirical Results
• Q3. How to select the best model from a set of trained models?
• A. Unsupervised (training) scores do not correlated to the disentanglement metrics
19
Unsupervised scores vs disentanglement metrics
20. Summary
• TL;DR: Current unsupervised learning of disentangled representation has a limitation!
• Summary of findings:
• Q1. Which method should be used?
• A. Current methods should be rigorously validated (no significant difference)
20
21. Summary
• TL;DR: Current unsupervised learning of disentangled representation has a limitation!
• Summary of findings:
• Q1. Which method should be used?
• A. Current methods should be rigorously validated (no significant difference)
• Q2. How to choose the hyperparameters?
• A. No rule of thumb, but transfer across datasets seem to help!
21
22. Summary
• TL;DR: Current unsupervised learning of disentangled representation has a limitation!
• Summary of findings:
• Q1. Which method should be used?
• A. Current methods should be rigorously validated (no significant difference)
• Q2. How to choose the hyperparameters?
• A. No rule of thumb, but transfer across datasets seem to help!
• Q3. How to select the best model from a set of trained models?
• A. (Unsupervised) model selection remains a key challenge!
22
23. Following Work & Future Direction
• “Disentangling Factors of Variation Using Few Labels”
(ICLR Workshop 2019, NeurIPS 2019 submission)
• Summary of findings: Using a few labels highly improves the disentanglement!
23
24. Following Work & Future Direction
• “Disentangling Factors of Variation Using Few Labels”
(ICLR Workshop 2019, NeurIPS 2019 submission)
• Summary of findings: Using a few labels highly improves the disentanglement!
1. Existing disentanglement metrics + few labels perform well on model selection,
even though models are completely trained in an unsupervised manner
24
25. Following Work & Future Direction
• “Disentangling Factors of Variation Using Few Labels”
(ICLR Workshop 2019, NeurIPS 2019 submission)
• Summary of findings: Using a few labels highly improves the disentanglement!
1. Existing disentanglement metrics + few labels perform well on model selection,
even though models are completely trained in an unsupervised manner
2. One can obtain even better results if one use few labels into the learning processes
(use a simple supervised regularizer)
25
26. Following Work & Future Direction
• “Disentangling Factors of Variation Using Few Labels”
(ICLR Workshop 2019, NeurIPS 2019 submission)
• Summary of findings: Using a few labels highly improves the disentanglement!
1. Existing disentanglement metrics + few labels perform well on model selection,
even though models are completely trained in an unsupervised manner
2. One can obtain even better results if one use few labels into the learning processes
(use a simple supervised regularizer)
• Take-home message: Future research should be on “how to utilize inductive bias better”
using a few labels, rather than the previous total correlation-like approaches
26