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533

03:19:44

061: Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST Yann LeCun thinks that it’s specious to say neural network models are interpolating because in high dimensions, everything is extrapolation. Recently Dr. Randall Bellestrerio, Dr. Jerome Pesente and prof. Yann LeCun released their paper learning in high dimensions always amounts to […]

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05:37

Two Minute Papers: Ubisoft’s New AI Predicts the Future of Virtual Characters! 🐺

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper “SuperTrack – Motion Tracking for Physically Simulated Characters using Supervised Learning” is available here: https://static-wordpress.akamaized.net/montreal.ubisoft.com/wp-content/uploads/2021/11/24183638/SuperTrack.pdf SuperTrack – Motion Tracking for Physically Simulated Characters using Supervised Learning ❤️ Watch these videos in early access on our Patreon page or join […]

05:06

CoRL 2020, Spotlight Talk 33: SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Ne…

“**SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks** Yan Xu (Chinese University of Hong Kong)*; Zhaoyang Huang (Zhejiang University); Kwan-Yee Lin (SenseTime Research); Xinge Zhu (The Chinese University of Hong Kong); Jianping Shi (Sensetime Group Limited); Hujun Bao (Zhejiang University); Guofeng Zhang (Zhejiang University); Hongsheng Li (Chinese University of Hong Kong) Publication: http://corlconf.github.io/paper_33/ **Abstract** […]

05:03

CoRL 2020, Spotlight Talk 426: ACNMP: Skill Transfer and Task Extrapolation through Learning from…

“**ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing** Mete Akbulut (Bogazici University)*; Erhan Oztop (Ozyeğin Üniversitesi); Muhammet Yunus Seker (Bogazici University); Hh X (University); Ahmet Tekden (Boğaziçi Üniversitesi); Emre Ugur (Bogazici University) Publication: http://corlconf.github.io/paper_426/ **Abstract** To equip robots with dexterous skills, an effective approach is to first […]

05:05

CoRL 2020, Spotlight Talk 464: Self-Supervised 3D Keypoint Learning for Ego-Motion Estimation

“**Self-Supervised 3D Keypoint Learning for Ego-Motion Estimation** Jiexiong Tang (KTH Royal Institute of Technology)*; Rareș Ambruș (Toyota Research Institute); Vitor Guizilini (Toyota Research Institute); Sudeep Pillai (Toyota Research Institute); Hanme Kim (Toyota Research Institute); Patric Jensfelt (Royal Institute of Technology); Adrien Gaidon (Toyota Research Institute) Publication: http://corlconf.github.io/paper_464/ **Abstract** Detecting and matching robust viewpoint-invariant keypoints is […]

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11:33

2021's Biggest Advancements in Artificial Intelligence

2021 has been an incredible year in terms of advancements in the field of Artificial Intelligence Technologies. AI has managed to gain new abilities and managed to achieve the futuristic feat of taking over several jobs which previously only humans could perform. Whether it’s self-supervised learning, custom AI accelerators or neuromorphic chips, the future of […]

05:04

CoRL 2020, Spotlight Talk 324: Multi-Modal Anomaly Detection for Unstructured and Uncertain Envir…

“**Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments** Tianchen Ji (University of Illinois at Urbana-Champaign)*; Sri Theja Vuppala (University of Illinois at Urbana-Champaign); Girish Chowdhary (University of Illinois at Urbana Champaign); Katherine Driggs-Campbell (University of Illinois at Urbana-Champaign) Publication: http://corlconf.github.io/paper_324/ **Abstract** To achieve high-levels of autonomy, modern robots require the ability to detect and recover […]

05:01

CoRL 2020, Spotlight Talk 465: Self-Supervised Learning of Scene-Graph Representations for Robotics

**Self-Supervised Learning of Scene-Graph Representations for Robotic Sequential Manipulation Planning** Son Nguyen (University Stuttgart)*; Ozgur Oguz (Uni. of Stuttgart & Max Planck Inst. for Intelligent Systems ); Valentin Hartmann (University of Stuttgart); Marc Toussaint (Technische Universität Berlin) Publication: http://corlconf.github.io/paper_465/ **Abstract** We present a self-supervised representation learning approach for visual reasoning and integrate it into a […]

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02:56

Two Minute Papers: AI-Based 3D Pose Estimation: Almost Real Time!

📝 The paper “3D Human Pose Machines with Self-supervised Learning” and its source code is available here: https://arxiv.org/abs/1901.03798 http://www.sysu-hcp.net/3d_pose_ssl/ https://github.com/chanyn/3Dpose_ssl.git ❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: 313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, […]

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01:16:40

Lecture 20 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive […]

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01:15:55

Lecture 19 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on the debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, […]

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01:16:38

Lecture 18 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics […]

245

01:17:00

Lecture 17 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, […]

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01:13:06

Lecture 16 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]

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01:17:18

Lecture 15 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]

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01:20:40

Lecture 14 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, […]

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01:14:57

Lecture 13 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include […]

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01:14:23

Lecture 12 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen’s inequality, mixture of Gaussians, and expectation-maximization. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement […]

Lecture 11 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning […]

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01:12:56

Lecture 10 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and […]

Lecture 9 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding’s inequalities. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement […]

Lecture 8 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and […]

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01:15:45

Lecture 7 | Machine Learning (Stanford)

Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/zJX/ Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, […]

Lecture 6 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive […]