論文紹介 Skill preferences
•
0 j'aime•206 vues
This document summarizes a research paper about using reinforcement learning to extract robotic skills from human feedback. The key points are: 1) Previous research on reward design for reinforcement learning requires significant engineering costs, while human-in-the-loop RL allows acquiring skills through interactive feedback during training to remove hand-engineered rewards. 2) This paper aims to address the issue that complex tasks require unrealistically large amounts of interactive feedback to acquire good policies. 3) The proposed method trains a preference classifier on a small subset of labeled data to learn human preferences, then trains an encoder-decoder to extract skills from trajectories while following the learned human preferences.
Signaler
Partager
Signaler
Partager
Télécharger pour lire hors ligne
Recommandé
Ml - A shallow dive
A very high level introduction to the field of Data Science, Artificial Intelligence. Covers an introduction to Supervised Learning, Unsupervised Learning, Deep Learning and Neural Networks. Given as part of Industry Lectures event at GVP College of Engineering
Overview of Machine Learning and Feature Engineering
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
Deep learning: the future of recommendations
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
Empirical Study on Collaborative Software in the field of Machine learning.pptx
Empirical Study on Collaborative Software in the field of Machine learning
Recommandé
Ml - A shallow dive
A very high level introduction to the field of Data Science, Artificial Intelligence. Covers an introduction to Supervised Learning, Unsupervised Learning, Deep Learning and Neural Networks. Given as part of Industry Lectures event at GVP College of Engineering
Overview of Machine Learning and Feature Engineering
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
Deep learning: the future of recommendations
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
Empirical Study on Collaborative Software in the field of Machine learning.pptx
Empirical Study on Collaborative Software in the field of Machine learning
林守德/Practical Issues in Machine Learning
Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS in EE department from National Taiwan University, an MS-EE from the University of Michigan, and an MS in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, merit paper award in TAAI 2010, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup 2016 Champion. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He receives the Young Scholars' Creativity Award from Foundation for the Advancement of Outstanding Scholarship and Ta-You Wu Memorial Award.
lec01.pptx
deep learningFeature selection using Deep Neural Networks March 18, 2016 CSI 991 Kevin Ham
2 Neural Networks Basics Perceptron Equivalent performance to least mean square algorithm (linear regression) Activation Function Sigmoid, Hyperbolic Tangent Multi Layer Perceptrons Chains of perceptrons, perform feature extraction Training the network Training set, validation set, generalization set Back propagation
3 Perceptron and Activation Function The basic building block of Neural Networks (1) Summation of weighted inputs Bias performs change in y-intercept Output is present when the activation Threshold is overcome Activation function must be differentiable w1 w3 w2 Activation function Input 1 Input 2 Input 3 output Bias
4 Multi-Layer Perceptrons and Training Classification with 20 node MLP NN (4) Feature extraction with 5 layered Convolutional Neural Network (2) Feature Extraction with MLP NN (4)
5 Article Objectives … we propose a supervised approach for task-aware selection of features using Deep Neural Networks (DNN) in the context of action recognition (e.g. walking, running, jumping). (1) … selected features are found to give better classification performance than the original high-dimensional features. (1) It is also shown that the classification performance of the proposed feature selection technique is superior to the low-dimensional representation obtained by principal component analysis (PCA). (1)
6 Methodology … analyze the contribution of each of the input dimensions to identify the features (inputs) important for classification (1) … to correctly analyze the contribution of an input feature, we study its activation potential (averaged over all training values of the input and hidden neurons) relative to the total activation potential (1) The higher the activation potential contribution of an input dimension, the more likely is its participation in hidden neuronal activity and consequently, classification. (1)Feature selection using Deep Neural Networks March 18, 2016 CSI 991 Kevin Ham
2 Neural Networks Basics Perceptron Equivalent performance to least mean square algorithm (linear regression) Activation Function Sigmoid, Hyperbolic Tangent Multi Layer Perceptrons Chains of perceptrons, perform feature extraction Training the network Training set, validation set, generalization set Back propagation
3 Perceptron and Activation Function The basic building block of Neural Networks (1) Summation of weighted inputs Bias performs change in y-intercept Output is present when the activation Threshold is overcome Activation function must be differentiable w1 w3 w2 Activation function Input 1 Input 2 Input 3 output Bias
4 Multi-Layer Perceptrons and Training Classification with 20 node MLP NN (4) Feature extraction with 5 layered Convolutional Neural Network (2) Feature Extraction with MLP NN (4)
5 Article Objectives … we propose a supervised approach fo
Scalable image recognition model with deep embedding
A work unfinished yet but shown great potential. This slide is for the college research result presentation competition use.
DeepLearning4J and Spark: Successes and Challenges - François Garillot
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
DeepLearning4J and Spark: Successes and Challenges - François Garillot
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
DeepLearning4J and Spark: Successes and Challenges - François Garillot
At the recent Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered this lightning talk to a sold-out crowd.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
Reinforcement Learning
1) Reinforcement Learning 개요
- Markov Decision Processes
- 기본적인 강화학습의 개념
2) Planning vs Reinforcement Learning
- Reinforcement Learning과 Planning의 차이
- Model Dynamics 학습
- TD(Temporal Difference) 기반의 Reinforcement Learning
3) Reinforcement Learning 실습
- 간단한 2차원 게임에서의 실습 리뷰
http://www.fastcampus.co.kr/data_workshop_dla/
MILA DL & RL summer school highlights
Deep Learning and Reinforcement Learning summer schools summary
26th June-6th July 2017, Montreal, Quebec
Things I learned. What was your favourite lesson?
Number Crunching in Python
"Number Crunching in Python": slides presented at EuroPython 2012, Florence, Italy
Slides have been authored by me and by Dr. Enrico Franchi.
Scientific and Engineering Computing, Numpy NDArray implementation and some working case studies are reported.
Optimization
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
Topic
- Frontier topics in Optimization
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Overview of Deep Learning for Image Analysis, including Convolutional Neural Nets and Deep Features.
Introduction to machine_learning
An introduction to machine learning. I gave a talk on this, the video can be found here:
http://www.techgig.com/expert-speak/Introduction-to-Machine-Learning-616
AI Technology Overview and Career Advice
An introductory overview of current AI technology and applications, and career advice for those who are interested in AI.
Introduction of Deep Reinforcement Learning
발표자: 곽동현(서울대 박사과정, 현 NAVER Clova)
강화학습(Reinforcement learning)의 개요 및 최근 Deep learning 기반의 RL 트렌드를 소개합니다.
발표영상:
http://tv.naver.com/v/2024376
https://youtu.be/dw0sHzE1oAc
Startup.Ml: Using neon for NLP and Localization Applications
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Contenu connexe
Similaire à 論文紹介 Skill preferences
林守德/Practical Issues in Machine Learning
Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS in EE department from National Taiwan University, an MS-EE from the University of Michigan, and an MS in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, merit paper award in TAAI 2010, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup 2016 Champion. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He receives the Young Scholars' Creativity Award from Foundation for the Advancement of Outstanding Scholarship and Ta-You Wu Memorial Award.
lec01.pptx
deep learningFeature selection using Deep Neural Networks March 18, 2016 CSI 991 Kevin Ham
2 Neural Networks Basics Perceptron Equivalent performance to least mean square algorithm (linear regression) Activation Function Sigmoid, Hyperbolic Tangent Multi Layer Perceptrons Chains of perceptrons, perform feature extraction Training the network Training set, validation set, generalization set Back propagation
3 Perceptron and Activation Function The basic building block of Neural Networks (1) Summation of weighted inputs Bias performs change in y-intercept Output is present when the activation Threshold is overcome Activation function must be differentiable w1 w3 w2 Activation function Input 1 Input 2 Input 3 output Bias
4 Multi-Layer Perceptrons and Training Classification with 20 node MLP NN (4) Feature extraction with 5 layered Convolutional Neural Network (2) Feature Extraction with MLP NN (4)
5 Article Objectives … we propose a supervised approach for task-aware selection of features using Deep Neural Networks (DNN) in the context of action recognition (e.g. walking, running, jumping). (1) … selected features are found to give better classification performance than the original high-dimensional features. (1) It is also shown that the classification performance of the proposed feature selection technique is superior to the low-dimensional representation obtained by principal component analysis (PCA). (1)
6 Methodology … analyze the contribution of each of the input dimensions to identify the features (inputs) important for classification (1) … to correctly analyze the contribution of an input feature, we study its activation potential (averaged over all training values of the input and hidden neurons) relative to the total activation potential (1) The higher the activation potential contribution of an input dimension, the more likely is its participation in hidden neuronal activity and consequently, classification. (1)Feature selection using Deep Neural Networks March 18, 2016 CSI 991 Kevin Ham
2 Neural Networks Basics Perceptron Equivalent performance to least mean square algorithm (linear regression) Activation Function Sigmoid, Hyperbolic Tangent Multi Layer Perceptrons Chains of perceptrons, perform feature extraction Training the network Training set, validation set, generalization set Back propagation
3 Perceptron and Activation Function The basic building block of Neural Networks (1) Summation of weighted inputs Bias performs change in y-intercept Output is present when the activation Threshold is overcome Activation function must be differentiable w1 w3 w2 Activation function Input 1 Input 2 Input 3 output Bias
4 Multi-Layer Perceptrons and Training Classification with 20 node MLP NN (4) Feature extraction with 5 layered Convolutional Neural Network (2) Feature Extraction with MLP NN (4)
5 Article Objectives … we propose a supervised approach fo
Scalable image recognition model with deep embedding
A work unfinished yet but shown great potential. This slide is for the college research result presentation competition use.
DeepLearning4J and Spark: Successes and Challenges - François Garillot
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
DeepLearning4J and Spark: Successes and Challenges - François Garillot
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
DeepLearning4J and Spark: Successes and Challenges - François Garillot
At the recent Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered this lightning talk to a sold-out crowd.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
Reinforcement Learning
1) Reinforcement Learning 개요
- Markov Decision Processes
- 기본적인 강화학습의 개념
2) Planning vs Reinforcement Learning
- Reinforcement Learning과 Planning의 차이
- Model Dynamics 학습
- TD(Temporal Difference) 기반의 Reinforcement Learning
3) Reinforcement Learning 실습
- 간단한 2차원 게임에서의 실습 리뷰
http://www.fastcampus.co.kr/data_workshop_dla/
MILA DL & RL summer school highlights
Deep Learning and Reinforcement Learning summer schools summary
26th June-6th July 2017, Montreal, Quebec
Things I learned. What was your favourite lesson?
Number Crunching in Python
"Number Crunching in Python": slides presented at EuroPython 2012, Florence, Italy
Slides have been authored by me and by Dr. Enrico Franchi.
Scientific and Engineering Computing, Numpy NDArray implementation and some working case studies are reported.
Optimization
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
Topic
- Frontier topics in Optimization
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Overview of Deep Learning for Image Analysis, including Convolutional Neural Nets and Deep Features.
Introduction to machine_learning
An introduction to machine learning. I gave a talk on this, the video can be found here:
http://www.techgig.com/expert-speak/Introduction-to-Machine-Learning-616
AI Technology Overview and Career Advice
An introductory overview of current AI technology and applications, and career advice for those who are interested in AI.
Introduction of Deep Reinforcement Learning
발표자: 곽동현(서울대 박사과정, 현 NAVER Clova)
강화학습(Reinforcement learning)의 개요 및 최근 Deep learning 기반의 RL 트렌드를 소개합니다.
발표영상:
http://tv.naver.com/v/2024376
https://youtu.be/dw0sHzE1oAc
Startup.Ml: Using neon for NLP and Localization Applications
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Similaire à 論文紹介 Skill preferences (20)
Scalable image recognition model with deep embedding
Scalable image recognition model with deep embedding
DeepLearning4J and Spark: Successes and Challenges - François Garillot
DeepLearning4J and Spark: Successes and Challenges - François Garillot
DeepLearning4J and Spark: Successes and Challenges - François Garillot
DeepLearning4J and Spark: Successes and Challenges - François Garillot
DeepLearning4J and Spark: Successes and Challenges - François Garillot
DeepLearning4J and Spark: Successes and Challenges - François Garillot
Learn to Build an App to Find Similar Images using Deep Learning- Piotr Teterwak
Learn to Build an App to Find Similar Images using Deep Learning- Piotr Teterwak
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Startup.Ml: Using neon for NLP and Localization Applications
Startup.Ml: Using neon for NLP and Localization Applications
Modern recommender system in large content website
Modern recommender system in large content website
Dernier
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Quantum Computing: Current Landscape and the Future Role of APIs
The current state of quantum computing and the role of APIs to take it mainstream.
The Art of the Pitch: WordPress Relationships and Sales
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
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.
Elevating Tactical DDD Patterns Through Object Calisthenics
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Video Streaming: Then, Now, and in the Future
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
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
Enhancing Performance with Globus and the Science DMZ
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Dernier (20)
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
The Metaverse and AI: how can decision-makers harness the Metaverse for their...
The Metaverse and AI: how can decision-makers harness the Metaverse for their...
Quantum Computing: Current Landscape and the Future Role of APIs
Quantum Computing: Current Landscape and the Future Role of APIs
The Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and Sales
Free Complete Python - A step towards Data Science
Free Complete Python - A step towards Data Science
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Elevating Tactical DDD Patterns Through Object Calisthenics
Elevating Tactical DDD Patterns Through Object Calisthenics
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
Enhancing Performance with Globus and the Science DMZ
Enhancing Performance with Globus and the Science DMZ
論文紹介 Skill preferences
- 1. 論⽂紹介 Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback ア ザ ラ シ D E C . 1 1 . 2 0 2 1
- 2. ⾃⼰紹介 • HN：アザラシ • 好きなボード：M5Stack，Raspberry Pi • 趣味：ロボット製作，⼭歩き，数学 • 興味：強化学習，四⾜歩⾏ロボット，微分幾何，圏論 • 専⾨：同次システム＠⾮線形制御理論 • 棲息地：鴨川
- 4. この論⽂の貢献 • (先⾏研究) 強化学習における報酬設計は，相当なエンジニアリングコストが課題 Human-in-the-loop RLは，訓練中に⼈との対話的なフィードバックを実施することで，ハ ンドメイドの報酬設計を不要にした • (この論⽂の課題) タスクの複雑さが増すと，適切な⽅策を獲得するまでに⾮現実的な数の ⼈との対話的フィードバックを必要とすることが課題 ⽐較的少ないフィードバック数で，⼈の好みだけでなく好みから抜け落ちたスキル抽出まで 実施する
- 5. ⼿法の概要 SAC
- 6. アルゴリズムのプロセス(1/2) Step 1: Collect offline dataset ℬ (Expert demo + Random policy) Step 2: Provide labels “good/bad” for 10% of ℬ 𝒟 Step 3: Train preference classifier 𝑃! for 𝒟 Step 4: Train decoder 𝑝"! and encoder 𝑞"" with 𝑃!
- 7. アルゴリズムのプロセス(2/2) Step 1: Execute yellow loop (if iteration % K == 0) Step 2: Execute red loop Step 3: Update Agent SAC actor 𝜋#! and critic 𝑄#" , 𝑄$ #" [Note] Use learned decoder 𝑝"! SAC
- 8. 𝑃!の学習 • Preference classifier 𝑃!(𝑦|𝜏), where 𝑦 ∈ 0, 1 , 𝜏 is trajectory(state-action) sequences • Update 𝜓 by maximizing loss function(cross entropy)： 𝔼 %,' ∼𝒟 𝑦 ⋅ log 𝑃! 𝜏 + 1 − 𝑦 ⋅ log 1 − 𝑃! 𝜏 • [Note] オフラインデータセットの部分集合にラベル付け(𝑦 ∈ 0,1 )したものから学習
- 9. enc, decの学習 • skill-encoder 𝑞"" 𝑧 𝜏 and skill-decoder 𝑝"! 𝑎*, … , 𝑎*+,-. 𝑠*, 𝑧 where 𝑧 ∈ 𝒵 is skill, 𝑠 ∈ 𝒮 is state, 𝑎 ∈ 𝒜 is action, 𝜏 is trajectory(state- action) sequences. • Update 𝑝"! and 𝑞"" by maximizing loss function(ELBO of 𝛽-VAE with Gaussian prior with 𝑃!) 𝔼'∼𝒟,/∼0#(/|') 𝑃! 𝜏 ℒ456789:4;6:<78 + 𝛽 ⋅ ℒ45=;>?4<@?:<78
- 10. " ℛ"の学習 • Update 𝜂 by minimizing loss function(binary cross-entropy): where is distribution of Bradley-Terry model. • [Note] 演算⼦ A ≻ B は，AがBよりも優先されることの意味．
- 15. 考察 • 👍 / 👎 する作業はやりたくないな • Human Interactionの分野はこれからの発展が楽しみ😃 • 試⾏回数はまだ多いなって印象