Professor Steve Roberts; The Bayesian Crowd: scalable information combinati...Ian Morgan
Professor Steve Roberts, Machine learning research group and Oxford-Man Institute + Alan Turing Institute. Steve gave this talk on the 24th January at the London Bayes Nets meetup.
Picked-up lists of GAN variants which provided insights to the community. (GANs-Improved GANs-DCGAN-Unrolled GAN-InfoGAN-f-GAN-EBGAN-WGAN)
After short introduction to GANs, we look through the remaining difficulties of standard GANs and their temporary solutions (Improved GANs). By following the slides, we can see the other solutions which tried to resolve the problems in various ways, e.g. careful architecture selection (DCGAN), slight change in update (Unrolled GAN), additional constraint (InfoGAN), generalization of the loss function using various divergence (f-GAN), providing new framework of energy based model (EBGAN), another step of generalization of the loss function (WGAN).
Continual Learning with Deep Architectures - Tutorial ICML 2021Vincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills (Parisi, 2019). However, despite early speculations and few pioneering works (Ring, 1998; Thrun, 1998; Carlson, 2010), very little research and effort has been devoted to address this vision. Current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for (Goodfellow, 2013). Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus.
In this tutorial, we propose to summarize the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI (Lomonaco, 2019). Starting from a motivation and a brief history, we link recent Continual Learning advances to previous research endeavours on related topics and we summarize the state-of-the-art in terms of major approaches, benchmarks and key results. In the second part of the tutorial we plan to cover more exploratory studies about Continual Learning with low supervised signals and the relationships with other paradigms such as Unsupervised, Semi-Supervised and Reinforcement Learning. We will also highlight the impact of recent Neuroscience discoveries in the design of original continual learning algorithms as well as their deployment in real-world applications. Finally, we will underline the notion of continual learning as a key technological enabler for Sustainable Machine Learning and its societal impact, as well as recap interesting research questions and directions worth addressing in the future.
Authors: Vincenzo Lomonaco, Irina Rish
Official Website: https://sites.google.com/view/cltutorial-icml2021
Professor Steve Roberts; The Bayesian Crowd: scalable information combinati...Ian Morgan
Professor Steve Roberts, Machine learning research group and Oxford-Man Institute + Alan Turing Institute. Steve gave this talk on the 24th January at the London Bayes Nets meetup.
Picked-up lists of GAN variants which provided insights to the community. (GANs-Improved GANs-DCGAN-Unrolled GAN-InfoGAN-f-GAN-EBGAN-WGAN)
After short introduction to GANs, we look through the remaining difficulties of standard GANs and their temporary solutions (Improved GANs). By following the slides, we can see the other solutions which tried to resolve the problems in various ways, e.g. careful architecture selection (DCGAN), slight change in update (Unrolled GAN), additional constraint (InfoGAN), generalization of the loss function using various divergence (f-GAN), providing new framework of energy based model (EBGAN), another step of generalization of the loss function (WGAN).
Continual Learning with Deep Architectures - Tutorial ICML 2021Vincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills (Parisi, 2019). However, despite early speculations and few pioneering works (Ring, 1998; Thrun, 1998; Carlson, 2010), very little research and effort has been devoted to address this vision. Current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for (Goodfellow, 2013). Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus.
In this tutorial, we propose to summarize the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI (Lomonaco, 2019). Starting from a motivation and a brief history, we link recent Continual Learning advances to previous research endeavours on related topics and we summarize the state-of-the-art in terms of major approaches, benchmarks and key results. In the second part of the tutorial we plan to cover more exploratory studies about Continual Learning with low supervised signals and the relationships with other paradigms such as Unsupervised, Semi-Supervised and Reinforcement Learning. We will also highlight the impact of recent Neuroscience discoveries in the design of original continual learning algorithms as well as their deployment in real-world applications. Finally, we will underline the notion of continual learning as a key technological enabler for Sustainable Machine Learning and its societal impact, as well as recap interesting research questions and directions worth addressing in the future.
Authors: Vincenzo Lomonaco, Irina Rish
Official Website: https://sites.google.com/view/cltutorial-icml2021
Recommendation system using collaborative deep learningRitesh Sawant
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional
CF-based methods use the ratings given to items by users
as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in
many applications, causing CF-based methods to degrade
significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as
item content information may be utilized. Collaborative
topic regression (CTR) is an appealing recent method taking
this approach which tightly couples the two components that
learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be
very effective when the auxiliary information is very sparse.
To address this problem, we generalize recent advances in
deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model
called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback)
matrix. Extensive experiments on three real-world datasets
from different domains show that CDL can significantly advance the state of the art.
Deep Learning and Reinforcement Learning summer schools summary
26th June-6th July 2017, Montreal, Quebec
Things I learned. What was your favourite lesson?
Artificial Intelligence Workshop, Collegio universitario Bertoni, Milano, 20 May 2017.
Audience of the workshop: undergraduate students without neural networks background.
Summary:
- Deep Learning Showcase
- What is deep learning and how it works
- How to start with deep learning
- Live demo: image recognition with Nvidia DIGITS
- Playground
Duration: 2 hours.
https://telecombcn-dl.github.io/drl-2020/
This course presents the principles of reinforcement learning as an artificial intelligence tool based on the interaction of the machine with its environment, with applications to control tasks (eg. robotics, autonomous driving) o decision making (eg. resource optimization in wireless communication networks). It also advances in the development of deep neural networks trained with little or no supervision, both for discriminative and generative tasks, with special attention on multimedia applications (vision, language and speech).
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
[GAN by Hung-yi Lee]Part 2: The application of GAN to speech and text processingNAVER Engineering
Generative Adversarial Network and its Applications on Speech and Natural Language Processing, Part 2.
발표자: Hung-yi Lee(국립 타이완대 교수)
발표일: 18.7.
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
In the first part of the talk, I will first give an introduction of GAN and provide a thorough review about this technology. In the second part, I will focus on the applications of GAN to speech and natural language processing. I will demonstrate the applications of GAN on voice I will also talk about the research directions towards unsupervised speech recognition by GAN.conversion, unsupervised abstractive summarization and sentiment controllable chat-bot.
AI&BigData Lab. Mostapha Benhenda. "Word vector representation and applications"GeeksLab Odessa
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Mostapha Benhenda (Organizer, Kyiv deep learning meetup)
«Word vector representations and applications» (on ENG)
Word vector representations are functions that map words to vectors, in a way that preserve their meaning. These vectors can then be fed to machine learning algorithms, with broad practical applications, including machine tranlation and sentiment analysis.
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Usage of Generative Adversarial Networks (GANs) in HealthcareGlobalLogic Ukraine
The presentation is devoted to the application of Generative Adversarial Networks (GANs) in Healthcare. We will shortly observe basic principles and features of such networks, outline the types of tasks in medicine researches and practice that can be solved with GANs. Than we’ll discuss the examples of GANs using for the solving for some medical tasks.
This presentation by Vladyslav Kolbasin (Lead Software Developer, Consultant, GlobalLogic, Kharkiv) was delivered at AI Ukraine 2017 (Kharkiv) on September 24, 2017.
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.
Recommendation system using collaborative deep learningRitesh Sawant
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional
CF-based methods use the ratings given to items by users
as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in
many applications, causing CF-based methods to degrade
significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as
item content information may be utilized. Collaborative
topic regression (CTR) is an appealing recent method taking
this approach which tightly couples the two components that
learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be
very effective when the auxiliary information is very sparse.
To address this problem, we generalize recent advances in
deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model
called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback)
matrix. Extensive experiments on three real-world datasets
from different domains show that CDL can significantly advance the state of the art.
Deep Learning and Reinforcement Learning summer schools summary
26th June-6th July 2017, Montreal, Quebec
Things I learned. What was your favourite lesson?
Artificial Intelligence Workshop, Collegio universitario Bertoni, Milano, 20 May 2017.
Audience of the workshop: undergraduate students without neural networks background.
Summary:
- Deep Learning Showcase
- What is deep learning and how it works
- How to start with deep learning
- Live demo: image recognition with Nvidia DIGITS
- Playground
Duration: 2 hours.
https://telecombcn-dl.github.io/drl-2020/
This course presents the principles of reinforcement learning as an artificial intelligence tool based on the interaction of the machine with its environment, with applications to control tasks (eg. robotics, autonomous driving) o decision making (eg. resource optimization in wireless communication networks). It also advances in the development of deep neural networks trained with little or no supervision, both for discriminative and generative tasks, with special attention on multimedia applications (vision, language and speech).
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
[GAN by Hung-yi Lee]Part 2: The application of GAN to speech and text processingNAVER Engineering
Generative Adversarial Network and its Applications on Speech and Natural Language Processing, Part 2.
발표자: Hung-yi Lee(국립 타이완대 교수)
발표일: 18.7.
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
In the first part of the talk, I will first give an introduction of GAN and provide a thorough review about this technology. In the second part, I will focus on the applications of GAN to speech and natural language processing. I will demonstrate the applications of GAN on voice I will also talk about the research directions towards unsupervised speech recognition by GAN.conversion, unsupervised abstractive summarization and sentiment controllable chat-bot.
AI&BigData Lab. Mostapha Benhenda. "Word vector representation and applications"GeeksLab Odessa
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Mostapha Benhenda (Organizer, Kyiv deep learning meetup)
«Word vector representations and applications» (on ENG)
Word vector representations are functions that map words to vectors, in a way that preserve their meaning. These vectors can then be fed to machine learning algorithms, with broad practical applications, including machine tranlation and sentiment analysis.
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Usage of Generative Adversarial Networks (GANs) in HealthcareGlobalLogic Ukraine
The presentation is devoted to the application of Generative Adversarial Networks (GANs) in Healthcare. We will shortly observe basic principles and features of such networks, outline the types of tasks in medicine researches and practice that can be solved with GANs. Than we’ll discuss the examples of GANs using for the solving for some medical tasks.
This presentation by Vladyslav Kolbasin (Lead Software Developer, Consultant, GlobalLogic, Kharkiv) was delivered at AI Ukraine 2017 (Kharkiv) on September 24, 2017.
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 lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
The Smart Way to Invest in Artificial Intelligence and Machine Learning: Lisha Li, Amplify Partners
AI and ML are seeping into every startup, at least into every pitch deck. But what does it mean to build an AI/ML company? Some startups do require a closet filled with five PhD’s in data science, but that doesn’t necessarily mean yours does. Building intelligently with AI and ML.
Building AI Applications using Knowledge GraphsAndre Freitas
Goals of this Tutorial:
Provide a broad view of the multiple perspectives underlying knowledge graphs.
Show knowledge graphs as a foundation for building AI systems.
Method:
Focus on the contemporary and emerging perspectives.
Sampling exemplar approaches and infrastructures on each of these emerging perspectives (not an exhaustive survey).
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Rsqrd AI - ML Interpretability: Beyond Feature ImportanceAlessya Visnjic
In this talk, Javier Antorán discusses the importance of uncertainty when it comes to ML interpretability. He offers a new uncertainty-based interpretability technique called CLUE and compares it to existing model interpretability techniques in two usability studies. Javier is a Ph.D. student at the University of Cambridge. His research interests include Bayesian deep learning, uncertainty in machine learning, representation learning, and information theory.
The Deep Continual Learning community should move beyond studying forgetting in Class-Incremental Learning Scenarios! In this tutorial we gave at
#CoLLAs2023, me and Antonio Carta try to explain why and how! 👇
Do you agree?
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180526@Taiwan AI Academy, Professional Managers Class.
Covering important concepts of classical machine learning, in preparation for deep learning topics to follow. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
ICSE’14 Workshop Keynote Address: Emerging Trends in Software Metrics (WeTSOM’14).
Data about software projects is not stored in metrc1, metric2,…,
but is shared between them in some shared, underlying,shape.
Not every project has thesame underlying simple shape; many projects have different,
albeit simple, shapes.
We can exploit that shape, to great effect: for better local predictions; for transferring
lessons learned; for privacy-preserving data mining/
Multi task learning stepping away from narrow expert models 7.11.18Cloudera, Inc.
Join this webinar as Friederike Schüür covers:
A conceptual introduction to multi-task learning (MTL), how and why it works
A technical deep dive, from MTL random forests to MTL neural networks
Applications of MTL, from structured data to text and images
The benefits of MTL to organizations, from financial services to healthcare and agriculture
Similar to Professor Steve Roberts; The Bayesian Crowd: scalable information combination for Citizen Science and Crowdsourcing (20)
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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
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.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
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Professor Steve Roberts; The Bayesian Crowd: scalable information combination for Citizen Science and Crowdsourcing
1. The Bayesian Crowd: scalable
informaon combinaon for Cizen
Science and Crowdsourcing
Stephen Roberts
Machine Learning Research Group Oxford-Man Instute
University of Oxford
Alan Turing Instute
Joint work with Edwin Simpson, Steven Reece Ma-eo Venanzi
Bayes Nets Meeng, January 2017
2.
3. • Bayesian modelling allows for explicit incorporation of all desiderata
• Effort focused not only on theory development, but algorithmic
implementations that are timely practical for real-world, real-time
scenarios
• Single, under- and over-arching philosophy…
“one method to rule them all… and in the darkness bind them”
“The language is that of Bayesian inference, which I will not utter
here...”
p(a|b) =
p(b|a)p(a)/p(b)
Core methodology – Bayesian inference
4. • Uncertainty at all levels of inference is naturally taken into account
• Optimal fusion of information: subjective, objective
• Handling missing values
• Handling of noise
• Principled inference of confidence and risk
• Optimal decision making
What does this buy us?
12. How can we deal with unreliable worker responses and
very large datasets?
Big data: Square Kilometer Array,
10 petabytes compressed images/day
Noisy reports: Twi-er, Typhoon Haiyan
13. Aims: Reliability and E9ciency
●
Challenge: volunteers have varying reliability
– Di;erent knowledge, interests, skills
– Typically handled with redundancy → build a consensus
●
Challenge: datasets are large, what to priorise?
●
Aim: increase accuracy by learning reliability
●
Aim: use our volunteers' me eciently
– Reduce redundant decisions
– Deploy experts where needed
– Use addional data to scale up to larger datasets
14. Machine Learning: aggregate responses and assign
tasks intelligently
●
Probabilisc models of people and data
●
Handle uncertainty in model
●
Opmise and automate analysis to reduce costs
Machine learning
Data
Crowd AnnotaonsCrowd
Results
15. Zooniverse has 26 current applicaons across a
range of domains, with 1 million volunteers
● Can we use ML to handle variaons in ability?
● Or to match tasks to people's interests and skills?
16. How can we combine annotaons from
di;erent members of the crowd?
● Fewer annotaons needed from more reliable labellers
● ConCdence and trust → user weights
● But weighted majority is soE selecon
– Blurred decision boundaries
● Need to combine di;erent experse + weak labellers
17. Bayesian Methods
● Opmal framework for combining evidence
● Quanfy prior beliefs explicitly
– E.g. workers are mostly be-er than random
● QuanCes uncertainty at all levels
– Which agents are reliable?
– Do we need more evidence for an object's target class?
● Principled approach
– Move away from Cne-tuning each project
– E.g. avoid trial-and-error thresholds to determine when
consensus reached
18. How can we aggregate responses intelligently?
● Bayes' rule combines di@erent pieces of informaon
● Weight workers' contribuons through their likelihood
of response to class
● Opmal weighted majority decision
● Error guarantees
● SoD selecon
p(t|c)∝p(t)∏k ∈K
p(c
(k)
|t)
p(c(k)
|t) c(k)
t
19. Likelihood deCned by a confusion matrix
● Likelihood = of response to class :
● Richer than user accuracy weights:
– Di;ering skill levels in each class
– Responses need not be votes
p(c(k)
|t)
Response c(k)
Target
class
t
A B C
1 0.7 0.1 0.2
2 0.4 0.4 0.2
π(k)
c
(k)
t
20. Independent Bayesian classiCer combinaon
(IBCC) handles parameter uncertainty
Target labels
(multinomial)
Observed worker responses
(multinomial)
Worker-
specific
confusion
matrix
(Dirichlet)
Proportions of each
class (Dirichlet)
●
Deal raonally with limited or missing data
21. Hyperparameters encode prior beliefs in worker
behaviour, e.g. worker is be-er than random
●
Opmise/marginalise to handle model uncertainty
●
Share prior pseudo-counts between similar projects
●
Rao → relave
probability of
agent
responses given
class t
●
Magnitude →
strength of
prior beliefs
c(k)
t
A B C
1 7 1 2
2 4 4 2
22. Joint, condi
oned on hyper-hyper parameters
Inference
Gibbs sampling – rather slow
Variaonal Bayes – o;ers fast inference, at
expense of approximaons
Inference
23. -ve free energy Kullback-Leibler divergence
Variational Bayes
28. Users rate each presented object which provides a score of
-1 : very unlikely SN object
1 : possible SN object
3 : likely SN object
(“true” labels obtained retrospectively via Palomar Transient Factory
spectrographic analysis)
Zooniverse: Galaxy Zoo Supernovae
29. IBCC-VB outperforms alternaves
Galaxy Zoo
Supernovae
AUC
IBCC-VB 0.90
Mean 0.65
Weighted Sum 0.64
Weighted Majority 0.58
Area under ROC curve defining better
solutions
31. Community detecon over E[π] matrices:
behaviour types among Zooniverse users
Sensible Extreme Random Opmist Pessimist
● vbIBCC provides insights into crowd behaviour using
Bayesian community analysis
● Design training to inOuence these types
● CommunityBCC builds these types into the model to
be-er predict new workers
32. CommunityBCC builds these disnct types into
the model to be-er understand new workers
● Priors constrain the
worker model
● Fewer examples needed
to learn reliabilies
33. Dynamic IBCC: behaviour changes as people
learn, get bored, move...
● Detect a worker's current state: aggregate correctly,
select suitable tasks, inOuence behaviour
Current state
35. “true” decision label
(multinomial)
Set of all observed decisions
(multinomial)
Dirichlet
Dirichlet
Agent specific
“confusion” matrix
time
What about dynamics?
36. Dynamic IBCC tracks changes to the confusion
matrix over me
● Bayes' Clter
esmates
evolving
Markov chain
● Assumpon:
unexpected
behaviour →
state changes
Galaxy Zoo Supernovae example volunteer
37. Dynamic IBCC tracks changes to the confusion
matrix over me
● Bayes' Clter
esmates
evolving
Markov chain
● Assumpon:
unexpected
behaviour →
state changes
Mechanical Turk document classiCcaon
39. Combining the crowd with features:
TREC Crowdsourcing Challenge
● IBCC + 2000 LDA features acng
as addional classiCers [11]
● Classify unlabelled documents
● Results:
– 0.81 AUC with only 16%
documents labelled at all
– 0.77 for next-best approach
– 1st place required mulple
labellings of all documents
40. BCCWords: an e9cient way to learn language
in new contexts
25,000 50,000 75,000 100,000 125,000 150,000
0.2
0.3
0.4
0.5
0.6
0.7
0.8
#labels
Accuracy
IBCC
CBCC
ScalBCCWords
MV(Textclassi+er
DawidSkene
MV
Votedistribution
CrowdFlower Tweet
Senment
Posive words about the
weather learnt by
BCCWords
BCCWords increases
accuracy with limited
labels
41. Unstructured data in social media: a rich
source of mely informaon
Real-me, local events – e.g. emergency reports aDer an
earthquake
Senment about products, health and social issues – e.g.
opinions about H1N1, product reviews
Butler 2013, Morrow et al. 2011
42. Understanding Textual Data Streams
● Turn unstructured data into reliable, machine-readable
informaon
● Automated classiBers struggle to understand diverse,
evolving language in new contexts
● Need new tools to resolve ambiguity and lack of
training data
Ushahidi – From Hai 2010 earthquake
Morrow et al. 2011
Categories of earthquake reports
Nepal, 2015, Quakemap.org
Gender
Kivran-Swaine et al., 2013
“Love” “Dude”
43. Interpreng Language through Crowdsourcing
● Biased and noisy interpretaons
● Scalability: the workers cannot label everything mulple mes
● New techniques needed to reduce the workload of labellers
using textual informaon
● How to learn a language model from unreliable judgements?
+
+
-
+
Repeve TasksRepeve Tasks Time Costs
44. Scenario: Senment Analysis of Tweets and
Reviews
Dataset Text Plaorm Sen
ment
Classes
No.
Documents
No.
Judgements
No.
Workers
2013
CrowdScale
shared
task
challenge
Tweets about
weather
CrowdFlower Posive
Negave
Neutral –
Not related X
Unknown ?
98,980 569,375 461
Rodrigues et
al., 2013
Ro-en
Tomatoes
Movie
Reviews
Amazon
Mechanical
Turk
Posive
Negave
5,000 27,747 203
“Morning sunshine”
09:18 PM June 7, 2011
“Is it rainy too?
Totally hate it”
10:05 PM June 7, 2011
“lovely sunny day”
10:06 PM June 7,
2011
45. Bayesian ClassiCer Combinaon with Words
BCCWords
●
Bayes' theorem provides a principled mathemacal
framework for classiCer combinaon
– Dawid Skene, 1979; Kim Ghahramani, 2012; Simpson et al., 2013;
Venanzi et al., 2014.
– Outperforms weighted majority vong etc.
+
+
-
+BCCWords
46. Bayesian ClassiCer Combinaon with Words
BCCWords
● Novel approach to combine weak signals from text
and crowd
– Model the reliability of members of the crowd
– Train a language model to reduce the number of
judgements needed
+
+
-
+BCCWords
47. Reliability of judgements deBned by a
confusion matrix for each worker
● DeBnes likelihood for worker k:
● Aggregate support for class c using Bayes' rule:
● Richer than weighng by overall accuracy:
– Accounts for bias and random noise
– Di@ering skill levels in each class
– Labels need not be votes for true class
p(label
(k)
|true class)
label(k)
True
class
+ve uncertain -ve
+ve 0.7 0.1 0.2
-ve 0.4 0.4 0.2
∏k∈K
p(label
(k)
|trueclass=c)
48. Likelihood of text features in each class: bag-of-
words
ωc=p(wordn|true class=c)
●
Words have di;erent likelihoods in each senment class
●
Prior distribuon over word likelihoods in each class
●
Learning posterior : update pseudo-counts as we observe words
in document of class c
Good, nice
More likely
Terrible
More likely
ωc
ωc
51. BCCWords: judgements are condioned on
true class
Confusion
Matrix
Judgement
Label
True Class
N documents
52. BCCWords: judgements and words are
condioned on the true class
Confusion
Matrix
Judgement
Label
True Class
Word
Likelihoods
Words
ωc
N documents
53. BCCWords: judgements and words are
condioned on the true class
Use Bayes' rule to infer true class
from labels and words
Confusion
Matrix
Judgement
Label
True Class
Word
Likelihoods
Words
ωc
N documents
… but we need to
learn the likelihoods
from true class
labels
54. Variaonal Bayes: learn confusion matrices, language
model and true class with limited training data
●
Computaonally e9cient: 20 mins for 500k judgements, 98k tweets
●
Iteravely updates each variable in turn, learning from latent structure
and any prior knowledge or training data
●
Algorithm can be distributed to constrain memory requirements
55. Experiments: Senment Analysis of Tweets and
Reviews
Dataset Text Plaorm Sen
ment
Classes
No.
Documents
No.
Judgements
No.
Workers
2013
CrowdScale
shared
task
challenge
Tweets about
weather
CrowdFlower Posive
Negave
Neutral –
Not related X
Unknown ?
98,980 569,375 461
Rodrigues et
al., 2013
Ro-en
Tomatoes
Movie
Reviews
Amazon
Mechanical
Turk
Posive
Negave
5,000 27,747 203
“Morning sunshine”
09:18 PM June 7, 2011
“Is it rainy too?
Totally hate it”
10:05 PM June 7, 2011
“lovely sunny day”
10:06 PM June 7,
2011
56. Language Model for Weather Senment
Posive NegaveMost Likely Words
Discriminave Words
57. Disnct worker types show the importance of
learning reliability
1
0.5
0
1
0.5
0
1
1
0.5
True
class Worker
Label
Probability
Good Worker Inaccurate Worker
CrowdLower Weather – 5 classes
58. Summary: BCCWords fuses subjecve
interpretaons to learn models of language in
the wild
● Important to account for skills and bias
of individuals in crowd
● Learns worker reliability and language
model in a single integrated inference
algorithm
● Uses textual informaon to reduce the
number of judgements required
● Bayesian inference
– Proven framework for fusing informaon
– Handles uncertainty in true class labels
and model itself
1
0.5
0
1
0.5
0
1
0.5
0
1
0.5
0
59. Moving towards e9cient learning with
Crowd in-the-Loop
● Turn masses of unstructured, heterogeneous data into
reliable, machine-readable informaon
● Use the model to choose who does what task
1
0.5
0
1
0.5
0
1
0.5
0
1
0.5
0
● Detect di;erent interpretaons of language between communies
in the crowd?
60. Intelligent agent-task assignment:
who should classify which object?
● Aim: direct crowd's e;ort to learn quickly cheaply
● Priorise tasks by considering their features and conCdence
in their classiCcaon
● Task choice depends on the workers available
● Maximise expected ulity
DynIBCC confusion matrix
describes individual skills
61. Ulity of response: informaon gain about
targets when DynIBCC is updated
● Naturally balances exploraon exploitaon
● Explore an agent's behaviour from silver tasks
– Objects already labelled conCdently by crowd
– Increases ulity of past responses
● Exploit an agent's skills to learn uncertain targets t
E[U τ (k ,i)]=E[ I (t ; ci
(k)
∣Dτ )]
Index of target object
Worker ID
Crowdsourced data
collected so far
Time index
62. Hiring and Cring algorithm makes greedy
assignments to reduce computaonal cost
● Hire for priority task that matches current skills
● Fire if new crowd members likely to do be-er
63. Loose crowds on the web in organisaons:
Disaster Response
● Extracng key informaon from noisy background
– Text: Twi-er, Ushahidi 15000 messages in a few weeks [8]
– Images: Satellite, Social Media
– Team communicaons, other agencies
● Locaons of emergencies:
– connuous target funcon
64. Bayesian crowdsourced heatmaps visualise
likely emergencies and informaon gaps
● Neighbouring reports related by spaal Gaussian
process (GP) classiCer
Κ
ti
Density of
emergencies
at (x,y)
Emergency
state at (x,y)
ci
(k)
π(k)
α0
(k)
Sigmoid funcon maps GP to Dirichlet
GP Variance
65. Bayesian crowdsourced heatmaps visualise
likely emergencies and informaon gaps
Ushahidi crowd + trusted report from Crst responder
67. Adapve training and movaon to create diverse
skills and smulate workers
●
Model worker preferences, rewards
●
Fast approximaons to future ulity
– Deduct cost of rewards
– Add retenon, work rate, reliability
– Target clusters of workers
●
Selecng tasks/training: consider person's
history
Apprenceship/Peer Training
Infer improvements in confusion
matrices from e;ect of task on others
68. Models for combining new data types target
funcons
● Targets have mulple dimensions
– Shapes in PlanetFour
● Poisson processes, event rates
– Malaria rates
69. Acvely switch types of tasks to opmise
learning from the crowd
● Select quesons from decision tree
● Labelling, comparing, marking features, grouping...
● Ulity varies: accuracy of responses, current model of
features...
34.556
Maximise
informaon
about t
...is like...
70. Learn how people make decisions by
acvely adapng tasks
● Improve automaon,
reduce work
● Select interacon mode or
quesons in the micro-task
● Maximise informaon given
current model
● Crowd-supervised feature
extracon, e.g. adapng
PCA to learn more useful
features from the crowd
Projecon
71. Summary: Bayesian models enable accurate
and scalable crowdsourcing across domains
● Quanfy uncertainty in data model worker behaviour
● Acvely learn from crowds using model of features
● Opportunies: opmisaon and learning to automate
with humans-in-the-loop
Machine
learning
Data
Crowd AnnotaonsCrowd
Results
72. ORCHID and Zooniverse collaborators worked
with Rescue Global to idenfy and then reCne
their crical informaon requirements.
• placement of life detectors and water
Clters within 50 mile radius of Kathmandu.
Crowd labelled 1200 Planet Labs satellite images
using Zooniverse soEware.
• Recruited 25 image labellers from within
Oxford University and Rescue Global sta;
(they worked hard over the bank holiday
weekend).
Folded in OpenStreetMap building density data
and inferred populaon density map using
ORCHID data processing algorithms.
Delivered map overlay to Rescue Global for
disseminaon to their CaDRA partners (SARaid,
Team Rubicon, CADENA).
29/04/15 to
2/05/15
02/05/15 to
20:13 GMT 05/05/15
00:15 GMT
06/05/15
05/05/15
25/04/15, 7.8 Earthquake in Gorkha District of Nepal
73. SoDware on Github
● h+p://www.robots.ox.ac.uk/~edwin/
– Please use and report bugs
● PyIBCC: IBCC-VB and DynIBCC-VB in Python 2
– Collaborang with Zooniverse
● MatlabIBCC: IBCC-VB and DynIBCC-VB in Matlab
Acknowledgements
● Uni of Southampton: Nick Jennings, Alex Rogers, Sarvapali
Ramchurn, Ma+eo Venanzi
● Oxford: Edwin Simpson, Steve Reece, Chris Linto+ Zooniverse team
● EPSRC (UK research council), the ORCHID project, Rescue Global,
MicrosoD, Zooniverse
74. References
[1] Dawid, A. P., Skene, A. M. (1979). Maximum likelihood esmaon of observer error-rates using the EM algorithm. Applied stascs, 20-28.
[2] Kim, H. C., Ghahramani, Z. (2012). Bayesian classiCer combinaon. In Internaonal conference on arCcial intelligence and stascs (pp. 619-
627).
[3] E. Simpson, S. Roberts, I. Psorakis, A. Smith and C. Linto- (2011). Bayesian Combinaon of Mulple, Imperfect ClassiCers. Proceedings of NIPS
2011 workshop
[4] Simpson, E., Roberts, S., Psorakis, I., Smith, A. (2013). Dynamic bayesian combinaon of mulple imperfect classiCers. In Decision Making and
Imperfecon (pp. 1-35). Springer.
[5] Psorakis, I., Roberts, S., Ebden, M., Sheldon, B. (2011). Overlapping Community Detecon using Bayesian Nonnegave Matrix Factorizaon.
Physical Review E, 83.
[6] Venanzi, M., Guiver, J., Kazai, G., Kohli, P., Shokouhi, M. (2014). Community-based bayesian aggregaon models for crowdsourcing. In
Proceedings of the 23rd internaonal conference on World wide web (pp. 155-164). Internaonal World Wide Web Conferences Steering
Commi-ee.
[7] E. Simpson, S. Roberts (2015 – to appear). Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems, Scalable Decision
Making: Uncertainty, Imperfecon, Deliberaon; Studies in Computaonal Intelligence, Springer
[8] N. Morrow, N. Mock, A. Papendieck, and N. Kocmich (2011). Independent Evaluaon of the Ushahidi Hai Project. Development Informaon
Systems., 8:2011.
[9] MacKay, David J. C. (1992). Informaon-based objecve funcons for acve data selecon. Neural computaon, 4(4):590–604.
[10]Chen, X., Benne-, P. N., Collins-Thompson, K., and Horvitz, E. (2013). Pairwise ranking aggregaon in a crowdsourced se`ng. In Proceedings of
the sixth ACM internaonal conference on Web search and data mining. ACM
[11]E. Simpson, S. Reece, A. Penta, G. Ramchurn, and S. Roberts (2012). Using a Bayesian Model to Combine LDA Features with Crowdsourced
Responses. In The Twenty-First Text REtrieval Conference (TREC 2012), Crowdsourcing Track, NIST.
[12]S. Nitzan, J. Paroush (1982). Opmal decision rules in uncertain dichotomous choice situaons. Internaonal Economic Review, 23(2):289–297,
1982.
[13]D. Berend, A. Kontorovich (2014). Consistency of Weighted Majority Votes. NIPS
[14]Y. Zhang, X. Chen, D. Zhou, M. Jordan (2014). Spectral methods meet EM: a Provable Opmal Algorithm for Crowdsourcing.