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[付費5元查看完整內容]神經網絡如何推理算法？DeepMind Petar等LoG 2022 《神經算法推理》教程，系統性講解神經網絡與經典算法結合

能夠可靠地執行算法計算的神經網絡可能對機器學習和理論計算機科學具有革命性的潛力。一方面，它們可以實現在深度學習模型中很少看到的那種外推泛化。另一方面，它們可以在以前認為不可訪問的輸入上運行經典算法。這兩個承諾都由神經算法推理藍圖指導，該藍圖最近在Petar Velickovic和Charles Blundell的立場論文中提出。理論上，這是一個非常優雅的對自然輸入進行推理的流程，它仔細地利用了深度神經網絡作為特征提取器的久經考驗的能力。實際上，我們到底走了多遠?在本教程中，我們旨在提供回答神經算法推理的三個關鍵問題所需的基礎:如何開發執行算法計算的神經網絡，如何在現實問題中部署這種神經網絡，以及如何深化其與經典算法的理論聯系。我們的教程將從頭開始，以一種具有基本計算機科學背景的任何人都可以訪問的方式。還將提供動手編碼片段，展示與會者如何在相關算法推理數據集(如CLRS)上直接發展他們在圖表示學習中的想法，然后在下游智能體中部署它們(如強化學習)。

Part I: Developing NAR (Petar Veli?kovi?)

Part II: Deploying NAR (Andreea Deac)

Part III: Deepening NAR (Andrew Dudzik)

參考文獻

Harris, TE. and Ross, FS. Fundamentals of a Method for Evaluating Rail Net Capacities. Project RAND Research Memorandum * Vlastelica, M., Paulus, A., Musil, V., Martius, G. and Rolínek, M. Differentiation of Blackbox Combinatorial Solvers. ICLR’20 * Hamrick, JB., Allen, KR., Bapst, V., Zhu, T., McKee, KR., Tenenbaum, JB. and Battaglia, PW. Relational inductive bias for physical construction in humans and machines. CogSci’18

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PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks

Machine Learning (ML) techniques facilitate automating malicious software (malware for short) detection, but suffer from evasion attacks. Many researchers counter such attacks in heuristic manners short of both theoretical guarantees and defense effectiveness. We hence propose a new adversarial training framework, termed Principled Adversarial Malware Detection (PAD), which encourages convergence guarantees for robust optimization methods. PAD lays on a learnable convex measurement that quantifies distribution-wise discrete perturbations and protects the malware detector from adversaries, by which for smooth detectors, adversarial training can be performed heuristically with theoretical treatments. To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD for enhancing the deep neural network-based measurement and malware detector. Experimental results on two Android malware datasets demonstrate: (i) the proposed method significantly outperforms the state-of-the-art defenses; (ii) it can harden the ML-based malware detection against 27 evasion attacks with detection accuracies greater than 83.45%, while suffering an accuracy decrease smaller than 2.16% in the absence of attacks; (iii) it matches or outperforms many anti-malware scanners in VirusTotal service against realistic adversarial malware.

[付費5元查看完整內容]【干貨書】強化學習算法，98頁pdf綜合講解人工智能和機器學習

強化學習是一種學習范式，它關注于如何學習控制一個系統，從而最大化表達一個長期目標的數值性能度量。強化學習與監督學習的區別在于，對于學習者的預測，只向學習者提供部分反饋。此外，預測還可能通過影響被控系統的未來狀態而產生長期影響。因此，時間起著特殊的作用。強化學習的目標是開發高效的學習算法，以及了解算法的優點和局限性。強化學習具有廣泛的實際應用價值，從人工智能到運籌學或控制工程等領域。在這本書中，我們重點關注那些基于強大的動態規劃理論的強化學習算法。我們給出了一個相當全面的學習問題目錄，描述了核心思想，關注大量的最新算法，然后討論了它們的理論性質和局限性。

Preface ix Acknowledgments xiii Markov Decision Processes 1 Value Prediction Problems 11 Control 37 For Further Exploration 63 Further reading 63 Applications 63 Software 64 Appendix: The Theory of Discounted Markovian Decision Processes 65 A.1 Contractions and Banach’s fixed-point theorem 65 A.2 Application to MDPs 69 Bibliography 73 Author's Biography 89

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[付費5元查看完整內容]【PKDD2020教程】可解釋人工智能XAI:算法到應用，200頁ppt

能夠解釋機器學習模型的預測在醫療診斷或自主系統等關鍵應用中是很重要的。深度非線性ML模型的興起，在預測方面取得了巨大的進展。然而，我們不希望如此高的準確性以犧牲可解釋性為代價。結果，可解釋AI (XAI)領域出現了，并產生了一系列能夠解釋復雜和多樣化的ML模型的方法。

在本教程中，我們結構化地概述了在深度神經網絡(DNNs)的背景下為XAI提出的基本方法。特別地，我們提出了這些方法的動機，它們的優點/缺點和它們的理論基礎。我們還展示了如何擴展和應用它們，使它們在現實場景中發揮最大的作用。

本教程針對的是核心和應用的ML研究人員。核心機器學習研究人員可能會有興趣了解不同解釋方法之間的聯系，以及廣泛的開放問題集，特別是如何將XAI擴展到新的ML算法。應用ML研究人員可能會發現，理解標準驗證程序背后的強大假設是很有趣的，以及為什么可解釋性對進一步驗證他們的模型是有用的。他們可能還會發現新的工具來分析他們的數據并從中提取見解。參與者將受益于技術背景(計算機科學或工程)和基本的ML訓練。

目錄內容：

Part 1: Introduction to XAI (WS) 可解釋人工智能

  • Motivations for XAI
  • Methods and Validation of XAI
  • The Clever Hans Effect

Part 2: Methods for Explaining DNNs (GM) 可解釋深度神經網絡方法

  • Self-Explainable DNNs
  • Perturbation-Based Explanation Techniques
  • Propagation-Based Explanation Techniques

Part 3: Implementation, Theory, Evaluation, Extensions (GM) 實現，理論、評價

  • Implementating XAI Techniques for DNNs
  • Theoretical Embedding of XAI
  • Desiderata of XAI Techniques and Evaluation
  • Extending XAI Beyond Heatmaps and DNNs

Part 4: Applications (WS) 應用

  • Walk-Through Examples
  • Debugging Large Datasets (Meta-Explanations and "Unhansing")
  • XAI in the Sciences
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[付費5元查看完整內容]【DeepMind深度學習課程】神經網絡基礎，104頁ppt，Neural Networks Foundations

自2006年以來，神經網絡是引發深度學習革命的模型，但它們的基礎可以追溯到20世紀60年代。在這堂課中，DeepMind研究科學家Wojciech Czarnecki將介紹這些模型如何操作、學習和解決問題的基礎知識。他還介紹了各種術語/命名慣例，為與會者進一步、更高級的會談做準備。最后，他簡要介紹了神經網絡設計和開發的更多研究方向。

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[付費5元查看完整內容]100+篇《自監督學習(Self-Supervised Learning)》論文最新合集

自監督學習(Self-Supervised Learning)是一種介于無監督和監督學習之間的一種新范式，旨在減少對大量帶注釋數據的挑戰性需求。它通過定義無注釋(annotation-free)的前置任務(pretext task)，為特征學習提供代理監督信號。jason718整理了關于自監督學習最新的論文合集，非常值得查看！

地址： //github.com/jason718/awesome-self-supervised-learning

A curated list of awesome Self-Supervised Learning resources. Inspired by , , , and

Why Self-Supervised?

Self-Supervised Learning has become an exciting direction in AI community.

  • Jitendra Malik: "Supervision is the opium of the AI researcher"
  • Alyosha Efros: "The AI revolution will not be supervised"
  • Yann LeCun: "self-supervised learning is the cake, supervised learning is the icing on the cake, reinforcement learning is the cherry on the cake"

Contributing

We Need You!

Please help contribute this list by contacting or add

Markdown format:

- Paper Name. 
  [[pdf]](link) 
  [[code]](link)
  - Author 1, Author 2, and Author 3. *Conference Year*

Table of Contents

Computer Vision

Survey

  • Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey.
    • Longlong Jing and Yingli Tian.

Image Representation Learning

Benchmark code

FAIR Self-Supervision Benchmark : various benchmark (and legacy) tasks for evaluating quality of visual representations learned by various self-supervision approaches.

2015

  • Unsupervised Visual Representation Learning by Context Prediction.

    • Doersch, Carl and Gupta, Abhinav and Efros, Alexei A. ICCV 2015

  • Unsupervised Learning of Visual Representations using Videos.

    • Wang, Xiaolong and Gupta, Abhinav. ICCV 2015

  • Learning to See by Moving.

    • Agrawal, Pulkit and Carreira, Joao and Malik, Jitendra. ICCV 2015

  • Learning image representations tied to ego-motion.

    • Jayaraman, Dinesh and Grauman, Kristen. ICCV 2015

2016

  • Joint Unsupervised Learning of Deep Representations and Image Clusters.

    • Jianwei Yang, Devi Parikh, Dhruv Batra. CVPR 2016

  • Unsupervised Deep Embedding for Clustering Analysis.

    • Junyuan Xie, Ross Girshick, and Ali Farhadi. ICML 2016

  • Slow and steady feature analysis: higher order temporal coherence in video.

    • Jayaraman, Dinesh and Grauman, Kristen. CVPR 2016

  • Context Encoders: Feature Learning by Inpainting.

    • Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A. CVPR 2016

  • Colorful Image Colorization.

    • Zhang, Richard and Isola, Phillip and Efros, Alexei A. ECCV 2016

  • Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles.

    • Noroozi, Mehdi and Favaro, Paolo. ECCV 2016

  • Ambient Sound Provides Supervision for Visual Learning.

    • Owens, Andrew and Wu, Jiajun and McDermott, Josh and Freeman, William and Torralba, Antonio. ECCV 2016

  • Learning Representations for Automatic Colorization.

    • Larsson, Gustav and Maire, Michael and Shakhnarovich, Gregory. ECCV 2016

  • Unsupervised Visual Representation Learning by Graph-based Consistent Constraints.

    • Li, Dong and Hung, Wei-Chih and Huang, Jia-Bin and Wang, Shengjin and Ahuja, Narendra and Yang, Ming-Hsuan. ECCV 2016

2017

  • Adversarial Feature Learning.

    • Donahue, Jeff and Krahenbuhl, Philipp and Darrell, Trevor. ICLR 2017

  • Self-supervised learning of visual features through embedding images into text topic spaces.

    • L. Gomez* and Y. Patel* and M. Rusi?ol and D. Karatzas and C.V. Jawahar. CVPR 2017

  • Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction.

    • Zhang, Richard and Isola, Phillip and Efros, Alexei A. CVPR 2017

  • Learning Features by Watching Objects Move.

    • Pathak, Deepak and Girshick, Ross and Dollar, Piotr and Darrell, Trevor and Hariharan, Bharath. CVPR 2017

  • Colorization as a Proxy Task for Visual Understanding.

    • Larsson, Gustav and Maire, Michael and Shakhnarovich, Gregory. CVPR 2017

  • DeepPermNet: Visual Permutation Learning.

    • Cruz, Rodrigo Santa and Fernando, Basura and Cherian, Anoop and Gould, Stephen. CVPR 2017

  • Unsupervised Learning by Predicting Noise.

    • Bojanowski, Piotr and Joulin, Armand. ICML 2017

  • Multi-task Self-Supervised Visual Learning.

    • Doersch, Carl and Zisserman, Andrew. ICCV 2017

  • Representation Learning by Learning to Count.

    • Noroozi, Mehdi and Pirsiavash, Hamed and Favaro, Paolo. ICCV 2017

  • Transitive Invariance for Self-supervised Visual Representation Learning.

    • Wang, Xiaolong and He, Kaiming and Gupta, Abhinav. ICCV 2017

  • Look, Listen and Learn.

    • Relja, Arandjelovic and Zisserman, Andrew. ICCV 2017

  • Unsupervised Representation Learning by Sorting Sequences.

    • Hsin-Ying Lee, Jia-Bin Huang, Maneesh Kumar Singh, and Ming-Hsuan Yang. ICCV 2017

2018

  • Unsupervised Feature Learning via Non-parameteric Instance Discrimination

    • Zhirong Wu, Yuanjun Xiong and X Yu Stella and Dahua Lin. CVPR 2018

  • Learning Image Representations by Completing Damaged Jigsaw Puzzles.

    • Kim, Dahun and Cho, Donghyeon and Yoo, Donggeun and Kweon, In So. WACV 2018

  • Unsupervised Representation Learning by Predicting Image Rotations.

    • Spyros Gidaris and Praveer Singh and Nikos Komodakis. ICLR 2018

  • Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization.

    • Ozsel Kilinc and Ismail Uysal. ICLR 2018

  • Improvements to context based self-supervised learning.

    • Terrell Mundhenk and Daniel Ho and Barry Chen. CVPR 2018

  • Self-Supervised Feature Learning by Learning to Spot Artifacts.

    • Simon Jenni and Universit?t Bern and Paolo Favaro. CVPR 2018

  • Boosting Self-Supervised Learning via Knowledge Transfer.

    • Mehdi Noroozi and Ananth Vinjimoor and Paolo Favaro and Hamed Pirsiavash. CVPR 2018

  • Cross-domain Self-supervised Multi-task Feature Learning Using Synthetic Imagery.

    • Zhongzheng Ren and Yong Jae Lee. CVPR 2018

  • ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids.

    • Dinesh Jayaraman*, UC Berkeley; Ruohan Gao, University of Texas at Austin; Kristen Grauman. ECCV 2018

  • Deep Clustering for Unsupervised Learning of Visual Features

    • Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze. ECCV 2018

  • Cross Pixel Optical-Flow Similarity for Self-Supervised Learning.

    • Aravindh Mahendran, James Thewlis, Andrea Vedaldi. ACCV 2018

2019

  • Representation Learning with Contrastive Predictive Coding.

    • Aaron van den Oord, Yazhe Li, Oriol Vinyals.

  • Self-Supervised Learning via Conditional Motion Propagation.

    • Xiaohang Zhan, Xingang Pan, Ziwei Liu, Dahua Lin, and Chen Change Loy. CVPR 2019

  • Self-Supervised Representation Learning by Rotation Feature Decoupling.

    • Zeyu Feng; Chang Xu; Dacheng Tao. CVPR 2019

  • Revisiting Self-Supervised Visual Representation Learning.

    • Alexander Kolesnikov; Xiaohua Zhai; Lucas Beye. CVPR 2019

  • AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data.

    • Liheng Zhang, Guo-Jun Qi, Liqiang Wang, Jiebo Luo. CVPR 2019

  • Unsupervised Deep Learning by Neighbourhood Discovery. . .

    • Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu. ICML 2019

  • Contrastive Multiview Coding.

    • Yonglong Tian and Dilip Krishnan and Phillip Isola.

  • Large Scale Adversarial Representation Learning.

    • Jeff Donahue, Karen Simonyan.

  • Learning Representations by Maximizing Mutual Information Across Views.

    • Philip Bachman, R Devon Hjelm, William Buchwalter

  • Selfie: Self-supervised Pretraining for Image Embedding.

    • Trieu H. Trinh, Minh-Thang Luong, Quoc V. Le

  • Data-Efficient Image Recognition with Contrastive Predictive Coding

    • Olivier J. He ?naff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord

  • Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

    • Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song. NeurIPS 2019

  • Boosting Few-Shot Visual Learning with Self-Supervision

    • pyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, and Matthieu Cord. ICCV 2019

  • Self-Supervised Generalisation with Meta Auxiliary Learning

    • Shikun Liu, Andrew J. Davison, Edward Johns. NeurIPS 2019

  • Wasserstein Dependency Measure for Representation Learning

    • Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet. NeurIPS 2019

  • Scaling and Benchmarking Self-Supervised Visual Representation Learning

    • Priya Goyal, Dhruv Mahajan, Abhinav Gupta, Ishan Misra. ICCV 2019

2020

  • A critical analysis of self-supervision, or what we can learn from a single image

    • Yuki M. Asano, Christian Rupprecht, Andrea Vedaldi. ICLR 2020

  • On Mutual Information Maximization for Representation Learning

    • Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic. ICLR 2020

  • Understanding the Limitations of Variational Mutual Information Estimators

    • Jiaming Song, Stefano Ermon. ICLR 2020

  • Automatic Shortcut Removal for Self-Supervised Representation Learning

    • Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen

  • Momentum Contrast for Unsupervised Visual Representation Learning

    • Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick. FAIR

  • A Simple Framework for Contrastive Learning of Visual Representations

    • Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton

  • ClusterFit: Improving Generalization of Visual Representations

    • Xueting Yan*, Ishan Misra*, Abhinav Gupta, Deepti Ghadiyaram**, Dhruv Mahajan**. CVPR 2020

  • Self-Supervised Learning of Pretext-Invariant Representations

    • Ishan Misra, Laurens van der Maaten. CVPR 2020

Video Representation Learning

  • Unsupervised Learning of Video Representations using LSTMs.

    • Srivastava, Nitish and Mansimov, Elman and Salakhudinov, Ruslan. ICML 2015

  • Shuffle and Learn: Unsupervised Learning using Temporal Order Verification.

    • Ishan Misra, C. Lawrence Zitnick and Martial Hebert. ECCV 2016

  • LSTM Self-Supervision for Detailed Behavior Analysis

    • Biagio Brattoli*, Uta Büchler*, Anna-Sophia Wahl, Martin E. Schwab, and Bj?rn Ommer. CVPR 2017

  • Self-Supervised Video Representation Learning With Odd-One-Out Networks.

    • Basura Fernando and Hakan Bilen and Efstratios Gavves and Stephen Gould. CVPR 2017

  • Unsupervised Learning of Long-Term Motion Dynamics for Videos.

    • Luo, Zelun and Peng, Boya and Huang, De-An and Alahi, Alexandre and Fei-Fei, Li. CVPR 2017

  • Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning.

    • Chuang Gan and Boqing Gong and Kun Liu and Hao Su and Leonidas J. Guibas. CVPR 2018

  • Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning.

    • Biagio Brattoli*, Uta Büchler*, and Bj?rn Ommer. ECCV 2018

  • Self-supervised learning of a facial attribute embedding from video.

    • Wiles, O., Koepke, A.S., Zisserman, A. BMVC 2018

  • Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles.

    • Kim, Dahun and Cho, Donghyeon and Yoo, Donggeun and Kweon, In So. AAAI 2019

  • Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics.

    • Jiangliu Wang; Jianbo Jiao; Linchao Bao; Shengfeng He; Yunhui Liu; Wei Liu. CVPR 2019

  • DynamoNet: Dynamic Action and Motion Network.

    • Ali Diba; Vivek Sharma, Luc Van Gool, Rainer Stiefelhagen. ICCV 2019

  • Learning Correspondence from the Cycle-consistency of Time.

    • Xiaolong Wang*, Allan Jabri* and Alexei A. Efros. CVPR 2019

  • Joint-task Self-supervised Learning for Temporal Correspondence.

    • Xueting Li*, Sifei Liu*, Shalini De Mello, Xiaolong Wang, Jan Kautz, and Ming-Hsuan Yang. NIPS 2019

Geometry

  • Self-supervised Learning of Motion Capture.

    • Tung, Hsiao-Yu and Tung, Hsiao-Wei and Yumer, Ersin and Fragkiadaki, Katerina. NIPS 2017

  • Unsupervised Learning of Depth and Ego-Motion from Video.

    • Zhou, Tinghui and Brown, Matthew and Snavely, Noah and Lowe, David G. CVPR 2017

  • Active Stereo Net: End-to-End Self-Supervised Learning for Active Stereo Systems.

    • Yinda Zhang*, Sean Fanello, Sameh Khamis, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, Vladimir Tankovich, Shahram Izadi, Thomas Funkhouser. ECCV 2018

  • Self-Supervised Relative Depth Learning for Urban Scene Understanding.

    • Huaizu Jiang*, Erik Learned-Miller, Gustav Larsson, Michael Maire, Greg Shakhnarovich. ECCV 2018

  • Geometry-Aware Learning of Maps for Camera Localization.

    • Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz. CVPR 2018

  • Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection.

    • David Novotny, Samuel Albanie, Diane Larlus, Andrea Vedaldi. CVPR 2018

  • Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry.

    • Muhammed Kocabas; Salih Karagoz; Emre Akbas. CVPR 2019

  • SelFlow: Self-Supervised Learning of Optical Flow.

    • Jiangliu Wang; Jianbo Jiao; Linchao Bao; Shengfeng He; Yunhui Liu; Wei Liu. CVPR 2019

  • Unsupervised Learning of Landmarks by Descriptor Vector Exchange.

    • James Thewlis, Samuel Albanie, Hakan Bilen, Andrea Vedaldi. ICCV 2019

Audio

  • Audio-Visual Scene Analysis with Self-Supervised Multisensory Features.

    • Andrew Owens, Alexei A. Efros. ECCV 2018

  • Objects that Sound.

    • R. Arandjelovi?, A. Zisserman. ECCV 2018

  • Learning to Separate Object Sounds by Watching Unlabeled Video.

    • Ruohan Gao, Rogerio Feris, Kristen Grauman. ECCV 2018

  • The Sound of Pixels.

    • Zhao, Hang and Gan, Chuang and Rouditchenko, Andrew and Vondrick, Carl and McDermott, Josh and Torralba, Antonio. ECCV 2018

  • Learnable PINs: Cross-Modal Embeddings for Person Identity.

    • Arsha Nagrani, Samuel Albanie, Andrew Zisserman. ECCV 2018

  • Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization.

    • Bruno Korbar,Dartmouth College, Du Tran, Lorenzo Torresani. NIPS 2018

  • Self-Supervised Generation of Spatial Audio for 360° Video.

    • Pedro Morgado, Nuno Nvasconcelos, Timothy Langlois, Oliver Wang. NIPS 2018

  • TriCycle: Audio Representation Learning from Sensor Network Data Using Self-Supervision

    • Mark Cartwright, Jason Cramer, Justin Salamon, Juan Pablo Bello. WASPAA 2019

Others

  • Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model.
    • Qixiang Ye, Tianliang Zhang, Qiang Qiu, Baochang Zhang, Jie Chen, Guillermo Sapiro. CVPR 2017
  • Free Supervision from Video Games.
    • Philipp Kr?henbühl. CVPR 2018
  • Fighting Fake News: Image Splice Detection via Learned Self-Consistency
    • Minyoung Huh*, Andrew Liu*, Andrew Owens, Alexei A. Efros. ECCV 2018
  • Self-supervised Tracking by Colorization (Tracking Emerges by Colorizing Videos).
    • Carl Vondrick*, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy. ECCV 2018
  • High-Fidelity Image Generation With Fewer Labels.
    • Mario Lucic*, Michael Tschannen*, Marvin Ritter*, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly.
  • Self-supervised Fitting of Articulated Meshes to Point Clouds.
    • Chun-Liang Li, Tomas Simon, Jason Saragih, Barnabás Póczos and Yaser Sheikh. CVPR 2019
  • SCOPS: Self-Supervised Co-Part Segmentation.
    • Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, and Jan Kautz. CVPR 2019
  • Self-Supervised GANs via Auxiliary Rotation Loss.
    • Ting Chen; Xiaohua Zhai; Marvin Ritter; Mario Lucic; Neil Houlsby. CVPR 2019
  • Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking.
    • Jae Shin Yoon; Takaaki Shiratori; Shoou-I Yu; Hyun Soo Park. CVPR 2019
  • Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations.
    • Wonhee Lee; Joonil Na; Gunhee Kim. CVPR 2019
  • Self-Supervised Convolutional Subspace Clustering Network.
    • Junjian Zhang; Chun-Guang Li; Chong You; Xianbiao Qi; Honggang Zhang; Jun Guo; Zhouchen Lin. CVPR 2019
  • Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation.
    • Xin Wang; Qiuyuan Huang; Asli Celikyilmaz; Jianfeng Gao; Dinghan Shen; Yuan-Fang Wang; William Yang Wang; Lei Zhang. CVPR 2019
  • Unsupervised 3D Pose Estimation With Geometric Self-Supervision.
    • Ching-Hang Chen; Ambrish Tyagi; Amit Agrawal; Dylan Drover; Rohith MV; Stefan Stojanov; James M. Rehg. CVPR 2019
  • Learning to Generate Grounded Image Captions without Localization Supervision.
    • Chih-Yao Ma; Yannis Kalantidis; Ghassan AlRegib; Peter Vajda; Marcus Rohrbach; Zsolt Kira.
  • VideoBERT: A Joint Model for Video and Language Representation Learning
    • Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid. ICCV 2019
  • S4L: Self-Supervised Semi-Supervised Learning
    • Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer
  • Countering Noisy Labels By Learning From Auxiliary Clean Labels
    • Tsung Wei Tsai, Chongxuan Li, Jun Zhu

Machine Learning

  • Self-taught Learning: Transfer Learning from Unlabeled Data.

    • Raina, Rajat and Battle, Alexis and Lee, Honglak and Packer, Benjamin and Ng, Andrew Y. ICML 2007

  • Representation Learning: A Review and New Perspectives.

    • Bengio, Yoshua and Courville, Aaron and Vincent, Pascal. TPAMI 2013.

Reinforcement Learning

  • Curiosity-driven Exploration by Self-supervised Prediction.

    • Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. ICML 2017

  • Large-Scale Study of Curiosity-Driven Learning.

    • Yuri Burda*, Harri Edwards*, Deepak Pathak*, Amos Storkey, Trevor Darrell and Alexei A. Efros

  • Playing hard exploration games by watching YouTube.

    • Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas. NIPS 2018

  • Unsupervised State Representation Learning in Atari.

    • Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre C?té, R Devon Hjelm. NeurIPS 2019

Robotics

2006

  • Improving Robot Navigation Through Self-Supervised Online Learning

    • Boris Sofman, Ellie Lin, J. Andrew Bagnell, Nicolas Vandapel, and Anthony Stentz

  • Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation

    • A. Lookingbill, D. Lieb, J. Rogers and J. Curry

2009

  • Learning Long-Range Vision for Autonomous Off-Road Driving
    • Raia Hadsell, Pierre Sermanet, Jan Ben, Ayse Erkan, Marco Scoffier, Koray Kavukcuoglu, Urs Muller, Yann LeCun

2012

  • Self-supervised terrain classification for planetary surface exploration rovers
    • Christopher A. Brooks, Karl Iagnemma

2014

  • Terrain Traversability Analysis Using Multi-Sensor Data Correlation by a Mobile Robot
    • Mohammed Abdessamad Bekhti, Yuichi Kobayashi and Kazuki Matsumura

2015

  • Online self-supervised learning for dynamic object segmentation

    • Vitor Guizilini and Fabio Ramos, The International Journal of Robotics Research

  • Self-Supervised Online Learning of Basic Object Push Affordances

    • Barry Ridge, Ales Leonardis, Ales Ude, Miha Denisa, and Danijel Skocaj

  • Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot

    • Tanis Mar, Vadim Tikhanoff, Giorgio Metta, and Lorenzo Natale

2016

  • Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance

    • Kevin van Hecke, Guido de Croon, Laurens van der Maaten, Daniel Hennes, and Dario Izzo

  • The Curious Robot: Learning Visual Representations via Physical Interactions.

    • Lerrel Pinto and Dhiraj Gandhi and Yuanfeng Han and Yong-Lae Park and Abhinav Gupta. ECCV 2016

  • Learning to Poke by Poking: Experiential Learning of Intuitive Physics.

    • Agrawal, Pulkit and Nair, Ashvin V and Abbeel, Pieter and Malik, Jitendra and Levine, Sergey. NIPS 2016

  • Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours.

    • Pinto, Lerrel and Gupta, Abhinav. ICRA 2016

2017

  • Supervision via Competition: Robot Adversaries for Learning Tasks.

    • Pinto, Lerrel and Davidson, James and Gupta, Abhinav. ICRA 2017

  • Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge.

    • Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr., Alberto Rodriguez, Jianxiong Xiao. ICRA 2017

  • Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation.

    • Ashvin Nair*, Dian Chen*, Pulkit Agrawal*, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine. ICRA 2017

  • Learning to Fly by Crashing

    • Dhiraj Gandhi, Lerrel Pinto, Abhinav Gupta IROS 2017

  • Self-supervised learning as an enabling technology for future space exploration robots: ISS experiments on monocular distance learning

    • K. van Hecke, G. C. de Croon, D. Hennes, T. P. Setterfield, A. Saenz- Otero, and D. Izzo

  • Unsupervised Perceptual Rewards for Imitation Learning.

    • Sermanet, Pierre and Xu, Kelvin and Levine, Sergey. RSS 2017

  • Self-Supervised Visual Planning with Temporal Skip Connections.

    • Frederik Ebert, Chelsea Finn, Alex X. Lee, Sergey Levine. CoRL2017

2018

  • CASSL: Curriculum Accelerated Self-Supervised Learning.

    • Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, Abhinav Gupta. ICRA 2018

  • Time-Contrastive Networks: Self-Supervised Learning from Video.

    • Pierre Sermanet and Corey Lynch and Yevgen Chebotar and Jasmine Hsu and Eric Jang and Stefan Schaal and Sergey Levine. ICRA 2018

  • Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation.

    • Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine. ICRA 2018

  • Learning Actionable Representations from Visual Observations.

    • Dwibedi, Debidatta and Tompson, Jonathan and Lynch, Corey and Sermanet, Pierre. IROS 2018

  • Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning.

    • Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser. IROS 2018

  • Visual Reinforcement Learning with Imagined Goals.

    • Ashvin Nair*, Vitchyr Pong*, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine.NeurIPS 2018

  • Grasp2Vec: Learning Object Representations from Self-Supervised Grasping.

    • Eric Jang*, Coline Devin*, Vincent Vanhoucke, Sergey Levine. CoRL 2018

  • Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning.

    • Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn. CoRL 2018

2019

  • Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry.

    • Mirko Nava, Jerome Guzzi, R. Omar Chavez-Garcia, Luca M. Gambardella, Alessandro Giusti. Robotics and Automation Letters

  • Learning Latent Plans from Play.

    • Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet

2020

  • Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video.
    • Oier Mees, Markus Merklinger, Gabriel Kalweit, Wolfram Burgard ICRA 2020

NLP

  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

    • Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. NAACL 2019 Best Long Paper

  • Self-Supervised Dialogue Learning

    • Jiawei Wu, Xin Wang, William Yang Wang. ACL 2019

  • Self-Supervised Learning for Contextualized Extractive Summarization

    • Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang. ACL 2019

  • A Mutual Information Maximization Perspective of Language Representation Learning

    • Lingpeng Kong, Cyprien de Masson d'Autume, Lei Yu, Wang Ling, Zihang Dai, Dani Yogatama. ICLR 2020

  • VL-BERT: Pre-training of Generic Visual-Linguistic Representations

    • Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai. ICLR 2020

ASR

  • Learning Robust and Multilingual Speech Representations

    • Kazuya Kawakami, Luyu Wang, Chris Dyer, Phil Blunsom, Aaron van den Oord

  • Unsupervised pretraining transfers well across languages

    • Morgane Riviere, Armand Joulin, Pierre-Emmanuel Mazare, Emmanuel Dupoux

  • wav2vec: Unsupervised Pre-Training for Speech Recognition

    • Steffen Schneider, Alexei Baevski, Ronan Collobert, Michael Auli. INTERSPEECH 2019

  • vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

    • Alexei Baevski, Steffen Schneider, Michael Auli. ICLR 2020

  • Effectiveness of self-supervised pre-training for speech recognition

    • Alexei Baevski, Michael Auli, Abdelrahman Mohamed

  • Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning

    • Alexander H. Liu, Tao Tu, Hung-yi Lee, Lin-shan Lee

  • Self-Training for End-to-End Speech Recognition

    • Jacob Kahn, Ann Lee, Awni Hannun. ICASSP 2020

  • Generative Pre-Training for Speech with Autoregressive Predictive Coding

    • Yu-An Chung, James Glass. ICASSP 2020

Talks

  • The power of Self-Learning Systems. Demis Hassabis (DeepMind).
  • Supersizing Self-Supervision: Learning Perception and Action without Human Supervision. Abhinav Gupta (CMU).
  • Self-supervision, Meta-supervision, Curiosity: Making Computers Study Harder. Alyosha Efros (UCB)
  • Unsupervised Visual Learning Tutorial. CVPR 2018
  • Self-Supervised Learning. Andrew Zisserman (Oxford & Deepmind).
  • Graph Embeddings, Content Understanding, & Self-Supervised Learning. Yann LeCun. (NYU & FAIR)
  • Self-supervised learning: could machines learn like humans? Yann LeCun @EPFL.
  • Week 9 (b): CS294-158 Deep Unsupervised Learning(Spring 2019). Alyosha Efros @UC Berkeley.

Thesis

  • Supervision Beyond Manual Annotations for Learning Visual Representations. Carl Doersch. .
  • Image Synthesis for Self-Supervised Visual Representation Learning. Richard Zhang. .
  • Visual Learning beyond Direct Supervision. Tinghui Zhou. .
  • Visual Learning with Minimal Human Supervision. Ishan Misra. .

Blog

  • Self-Supervised Representation Learning. Lilian Weng. .
  • The Illustrated Self-Supervised Learning. Amit Chaudhary.

License

To the extent possible under law, has waived all copyright and related or neighboring rights to this work.

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[付費5元查看完整內容]多倫多大學2020春季CSC311課程「機器學習導論」，學習ML基礎知識

【導讀】2020新年伊始，多倫多大學Amir-massoud Farahmand和Emad A. M. Andrews博士開設了機器學習導論課程，介紹了機器學習的主要概念和思想，并概述了許多常用的機器學習算法。它還可以作為更高級的ML課程的基礎。

課程地址：

//amfarahmand.github.io/csc311/

機器學習(ML)是一組技術，它允許計算機從數據和經驗中學習，而不需要人工指定所需的行為。ML在人工智能作為一個學術領域和工業領域都變得越來越重要。本課程介紹了機器學習的主要概念和思想，并概述了許多常用的機器學習算法。它還可以作為更高級的ML課程的基礎。

本課程結束時，學生將學習(大致分類)

  • 機器學習問題:監督(回歸和分類)，非監督(聚類，降維)，強化學習

  • 模型:線性和非線性(基擴展和神經網絡)

  • 損失函數:平方損失、交叉熵、鉸鏈、指數等。

  • Regularizers: l1和l2

  • 概率觀點:最大似然估計，最大后驗，貝葉斯推理

  • 偏差和方差的權衡

  • 集成方法:Bagging 和 Boosting

  • ML中的優化技術: 梯度下降法和隨機梯度下降法

課程目錄：

參考資料：

(ESL) Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, 2009.

(PRML) Christopher M. Bishop, Pattern Recognition and Machine Learning, 2006.

(RL) Richard S. Sutton and Andrew G. Barto Reinforcement Learning: An Introduction, 2018.

(DL) Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016), Deep Learning

(MLPP) Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, 2013.

(ISL) Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, Introduction to Statistical Learning, 2017.

() Shai Shalev-Shwartz and Shai Ben-David Understanding Machine Learning: From Theory to Algorithms, 2014.

(ITIL) David MacKay, Information Theory, Inference, and Learning Algorithms, 2003.

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