Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Terry Taewoong Um
[Title] Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018)
[Authors] Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
[Link] https://arxiv.org/abs/1805.12114
* This paper is accepted for the spotlight session at NIPS 2018
This presentation includes some of the contents related to the paper, "Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning", Nagabandi et al. (ICML 2018).
A brief summary of Lie group formulation for robot mechanics. For more details, please refer to the book, "A first course in robot mechanics" written by Frank C. Park from the follow link.
http://robotics.snu.ac.kr/fcp/files/_pdf_files_publications/a_first_coruse_in_robot_mechanics.pdf
(http://terryum.io)
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
Learning with side information through modality hallucination (2016)Terry Taewoong Um
Learning with side information through modality hallucination, J. Hoffman et al., CVPR2016
http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Hoffman_Learning_With_Side_CVPR_2016_paper.html
"On human motion prediction using recurrent neural networks", Julieta Martinez, Michael J. Black, Javier Romero. CVPR2017
https://arxiv.org/abs/1705.02445
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Terry Taewoong Um
[Title] Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018)
[Authors] Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
[Link] https://arxiv.org/abs/1805.12114
* This paper is accepted for the spotlight session at NIPS 2018
This presentation includes some of the contents related to the paper, "Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning", Nagabandi et al. (ICML 2018).
A brief summary of Lie group formulation for robot mechanics. For more details, please refer to the book, "A first course in robot mechanics" written by Frank C. Park from the follow link.
http://robotics.snu.ac.kr/fcp/files/_pdf_files_publications/a_first_coruse_in_robot_mechanics.pdf
(http://terryum.io)
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
Learning with side information through modality hallucination (2016)Terry Taewoong Um
Learning with side information through modality hallucination, J. Hoffman et al., CVPR2016
http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Hoffman_Learning_With_Side_CVPR_2016_paper.html
"On human motion prediction using recurrent neural networks", Julieta Martinez, Michael J. Black, Javier Romero. CVPR2017
https://arxiv.org/abs/1705.02445
"Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data",
Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt, ICLR2017.
[Link] https://arxiv.org/abs/1605.06432
Novel Machine Learning Methods for Extraction of Features Characterizing Data...Velimir (monty) Vesselinov
Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models, AGU Fall meeting, Washington D.C., 2018.
Joint contrastive learning with infinite possibilitiestaeseon ryu
Contrastive Learning은 두 이미지가 유사한지 유사하지 않은 지에 대해서 어떤 label이 없이 피쳐들을 배우게 하는 머신 learning 테크닉 중에 하나입니다 우리는 기존에 있는 Supervised learning과 조금 차이가 있는데 Supervised learning은 label cost가 들고
그다음에 Task specific 하기 때문에 generalizability가 조금 떨어질 수 있습니다 하지만 Contrastive Learning은 label이 없이 진행하기때문에 label cost가 없고 generalizability가 조금 더 좋을수 있습니다. 해당 논문은 보다 유용한 Contrastive Learning을 위한 Joint Contrastive Learning에 대해 제안을 하는대요 https://youtu.be/0NLq-ikBP1I
A Reinforcement Learning Approach for Hybrid Flexible Flowline Scheduling Pro...Bert Van Vreckem
Paper presented at MISTA2013, Gent.
In this paper, we present a method based on Learning Automata to solve Hybrid Flexible Flowline Scheduling Problems (HFFSP) with additional constraints like sequence dependent setup times, precedence relations between jobs and machine eligibility. This category of production scheduling problems is noteworthy because it involves several types of constraints that occur in complex real-life production scheduling problems like those in process industry and batch production. In the proposed technique, Learning Automata play a dispersion game to determine the order of jobs to be processed in a way that makespan is minimized, and precedence constraint violations are avoided. Experiments on a set of benchmark problems indicate that this method can yield better results than the ones known until now.
GStreamer-VAAPI: Hardware-accelerated encoding and decoding on Intel hardware...Igalia
By Víctor M. Jáquez.
Slides at https://github.com/01org/gstreamer-vaapi/tree/master/docs/slides/gstconf2015
GStreamer-VAAPI is a set of GStreamer elements (vaapidecode, vaapipostroc, vaapisink, and several encoders) and libgstvapi, a library that wraps libva under a GObject/GStreamer semantics.
This talk will be about VAAPI and its integration with GStreamer. We will show a general overview of VAAPI architecture, the role of libgstvaapi, and finally, the design of GStreamer elements. Afterwards we will show what is ahead in the development of GStreamer-VAAPI, and the current problems and challenges.
알파고의 작동 원리를 설명한 슬라이드입니다.
English version: http://www.slideshare.net/ShaneSeungwhanMoon/how-alphago-works
- 비전공자 분들을 위한 티저: 바둑 인공지능은 과연 어떻게 만들까요? 딥러닝 딥러닝 하는데 그게 뭘까요? 바둑 인공지능은 또 어디에 쓰일 수 있을까요?
- 전공자 분들을 위한 티저: 알파고의 main components는 재밌게도 CNN (Convolutional Neural Network), 그리고 30년 전부터 유행하던 Reinforcement learning framework와 MCTS (Monte Carlo Tree Search) 정도입니다. 새로울 게 없는 재료들이지만 적절히 활용하는 방법이 신선하네요.
2016 아이펀팩토리 Dev Day 발표 자료
강연 제목 : Docker 로 Linux 없이 Linux 환경에서 개발하기
발표자 : 김진욱 CTO
<2016>
- 일시 : 2016년 9월 28 수요일 12:00~14:20
- 장소 : 넥슨 판교 사옥 지하 1층 교육실
"Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data",
Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt, ICLR2017.
[Link] https://arxiv.org/abs/1605.06432
Novel Machine Learning Methods for Extraction of Features Characterizing Data...Velimir (monty) Vesselinov
Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models, AGU Fall meeting, Washington D.C., 2018.
Joint contrastive learning with infinite possibilitiestaeseon ryu
Contrastive Learning은 두 이미지가 유사한지 유사하지 않은 지에 대해서 어떤 label이 없이 피쳐들을 배우게 하는 머신 learning 테크닉 중에 하나입니다 우리는 기존에 있는 Supervised learning과 조금 차이가 있는데 Supervised learning은 label cost가 들고
그다음에 Task specific 하기 때문에 generalizability가 조금 떨어질 수 있습니다 하지만 Contrastive Learning은 label이 없이 진행하기때문에 label cost가 없고 generalizability가 조금 더 좋을수 있습니다. 해당 논문은 보다 유용한 Contrastive Learning을 위한 Joint Contrastive Learning에 대해 제안을 하는대요 https://youtu.be/0NLq-ikBP1I
A Reinforcement Learning Approach for Hybrid Flexible Flowline Scheduling Pro...Bert Van Vreckem
Paper presented at MISTA2013, Gent.
In this paper, we present a method based on Learning Automata to solve Hybrid Flexible Flowline Scheduling Problems (HFFSP) with additional constraints like sequence dependent setup times, precedence relations between jobs and machine eligibility. This category of production scheduling problems is noteworthy because it involves several types of constraints that occur in complex real-life production scheduling problems like those in process industry and batch production. In the proposed technique, Learning Automata play a dispersion game to determine the order of jobs to be processed in a way that makespan is minimized, and precedence constraint violations are avoided. Experiments on a set of benchmark problems indicate that this method can yield better results than the ones known until now.
GStreamer-VAAPI: Hardware-accelerated encoding and decoding on Intel hardware...Igalia
By Víctor M. Jáquez.
Slides at https://github.com/01org/gstreamer-vaapi/tree/master/docs/slides/gstconf2015
GStreamer-VAAPI is a set of GStreamer elements (vaapidecode, vaapipostroc, vaapisink, and several encoders) and libgstvapi, a library that wraps libva under a GObject/GStreamer semantics.
This talk will be about VAAPI and its integration with GStreamer. We will show a general overview of VAAPI architecture, the role of libgstvaapi, and finally, the design of GStreamer elements. Afterwards we will show what is ahead in the development of GStreamer-VAAPI, and the current problems and challenges.
알파고의 작동 원리를 설명한 슬라이드입니다.
English version: http://www.slideshare.net/ShaneSeungwhanMoon/how-alphago-works
- 비전공자 분들을 위한 티저: 바둑 인공지능은 과연 어떻게 만들까요? 딥러닝 딥러닝 하는데 그게 뭘까요? 바둑 인공지능은 또 어디에 쓰일 수 있을까요?
- 전공자 분들을 위한 티저: 알파고의 main components는 재밌게도 CNN (Convolutional Neural Network), 그리고 30년 전부터 유행하던 Reinforcement learning framework와 MCTS (Monte Carlo Tree Search) 정도입니다. 새로울 게 없는 재료들이지만 적절히 활용하는 방법이 신선하네요.
2016 아이펀팩토리 Dev Day 발표 자료
강연 제목 : Docker 로 Linux 없이 Linux 환경에서 개발하기
발표자 : 김진욱 CTO
<2016>
- 일시 : 2016년 9월 28 수요일 12:00~14:20
- 장소 : 넥슨 판교 사옥 지하 1층 교육실
3월 22일 카이스트 전산학부에서 진행된 AI x Education 포럼의 발표 내용입니다.
대학은 과연 최적화된 교육을 제공하고 있을까요? 인공지능 기술을 배우려면 꼭 대학원에 가야 할까요?
이 영상을 보시면 제가 요즘 어떤 교육을 꿈꾸고 어떤 일들을 벌이고 있는지 아실 수 있을 것입니다.
인공지능/로보틱스 기술을 배우는 가장 쉬운 길, ART Lab 유튜브 채널의 구독, 좋아요 부탁드려요~!
https://www.youtube.com/channel/UCzypbmDj_kVPDW3qWlrEFjA
A brief introduction to OCR (Optical character recognition)Terry Taewoong Um
These slides include the answers for the following questions:
- What is OCR?
- Why do we need it?
- Why is it difficult?
- Comparison between OCR & object detections
- Three approaches for text localization
- Three approaches for text recognition
Videos are also available from the below:
(Korean) https://youtu.be/ckRFBl_XWFg
(English) coming soon
[Reference] Hwalsuk Lee, https://www.slideshare.net/deview/111-ai
Deep learning (Machine learning) tutorial for beginnersTerry Taewoong Um
비전공자들을 위한 머신러닝 / 딥러닝 튜토리얼입니다.
This is a deep learning (machine learning) tutorial for beginners.
Contents
1. Introduction to machine learning & deep learning
2. DL methods:
Convolutional neural networks (CNN)
Recurrent neural networks (RNN)
Variational autoencoder (VAE)
Generative adversarial networks (GAN)
3. Can we believe deep neural networks?
이 슬라이드는 부산 동아대학교에서 2018년 7월 16일 2시간 특강을 위해 마련된 자료로, 비전공자들을 위해 수식보다 개념 이해를 위해 힘쓴 강의자료입니다. 나중에 테리의 딥러닝톡에서도 한번 설명을 붙여볼게요~ https://www.facebook.com/deeplearningtalk/
https://www.youtube.com/playlist?list=PL0oFI08O71gKEXITQ7OG2SCCXkrtid7Fq
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Understanding Black-box Predictions via Influence Functions (2017)
1. Terry Taewoong Um (terry.t.um@gmail.com)
University of Waterloo
Department of Electrical & Computer Engineering
Terry T. Um
UNDERSTANDING BLACK-BOX PRED
-ICTION VIA INFLUENCE FUNCTIONS
1
3. QUESTIONS
Terry Taewoong Um (terry.t.um@gmail.com)
• How can we explain the predictions of a black-box model?
• Why did the system make this prediction?
• How can we explain where the model came from?
• What would happen if the values of a training point where
slightly changed?
4. INTERPRETATION OF DL RESULTS
Terry Taewoong Um (terry.t.um@gmail.com)
• Retrieving images that maximally activate a neuron [Girshick et al. 2014]
• Finding the most influential part from the image [Zhou et al. 2016]
• Learning a simpler model around a test point [Ribeiro et al. 2016]
But, they assumed a
fixed model
My NN is a function
of training inputs
5. INFLUENCE OF A TRAINING POINT
Terry Taewoong Um (terry.t.um@gmail.com)
• What is the influence of a training example for
the model (or for the loss of a test example)?
Optimal model param. :
Model param. by training w/o z :
Model param. by upweighting z :
without z == (𝜖 = −
1
𝑛
)
• The influence of upweighting z on the parameters 𝜃
6. INFLUENCE OF A TRAINING POINT
• Influence vs. Euclidean distance
7. INFLUENCE OF A TRAINING POINT
Terry Taewoong Um (terry.t.um@gmail.com)
• The influence of upweighting z on the parameters 𝜃
• The influence of upweighting z on the loss at a test point
8. PERTURBING A TRAINING POINT
Terry Taewoong Um (terry.t.um@gmail.com)
• Move 𝜖 mass from 𝑧 to 𝑧 𝛿
• If x is continuous and 𝛿 is small
• The effect of 𝑧 𝑧 𝛿 on the loss at a test point
9. SUMMARY
Terry Taewoong Um (terry.t.um@gmail.com)
• The influence of 𝑧 𝑧 𝛿 on the loss at a test point
• The influence of upweighting z on the parameters 𝜃
• The influence of upweighting z on the loss at a test point
10. EXAMPLE
Terry Taewoong Um (terry.t.um@gmail.com)
• The influence of upweighting z
• In logistic regression,
• Test : 7, Train : 7 (green), 1 (red)
11. SEVERAL PROBLEMS
Terry Taewoong Um (terry.t.um@gmail.com)
• Calculation of
Use Hessian-vector products (HVPs)
precompute 𝑠𝑡𝑒𝑠𝑡 by optimizing
or sampling-based approximation
12. SEVERAL PROBLEMS
Terry Taewoong Um (terry.t.um@gmail.com)
• What if is non-convex, so H < 0
Assuming that is a local minimum point, define a quadratic loss
Then calculate using the above
empirically working!
• Influence function vs. retraining
13. SEVERAL PROBLEMS
Terry Taewoong Um (terry.t.um@gmail.com)
• What if is non-differentiable?
e.g.) hinge loss
Use a differentiable variation of the hinge loss