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Accelerating Your AI Career

Welcome! – This is an interactive panel discussion on Accelerating Your AI Career. In this session, you will meet industry and academic leaders, and hear their stories and insights on career professional development in AI and the future of AI. Special thanks to Coursera and Stanford Online for co-hosting the event with us! We will […]

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#76 – LUKAS BIEWALD (CEO Weights and Biases)

Check out Weights and Biases here! https://wandb.me/MLST Lukas Biewald is an entrepreneur living in San Francisco. He was the founder and CEO of Figure Eight an Internet company that collects training data for machine learning.  In 2018, he founded Weights and Biases, a company that creates developer tools for machine learning. Recently WandB got a cash […]

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#74 Dr. ANDREW LAMPINEN – Symbolic behaviour in AI [UNPLUGGED]

Please note that in this interview Dr. Lampinen was expressing his personal opinions and they do not necessarily represent those of DeepMind. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Pod version: https://anchor.fm/machinelearningstreettalk/episodes/74-Dr–ANDREW-LAMPINEN—Symbolic-behaviour-in-AI-UNPLUGGED-e1h6far Dr. Andrew Lampinen is a Senior Research Scientist at DeepMind, and he thinks that symbols are subjective in the relativistic sense. Dr. Lampinen completed his PhD […]

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Katie Driggs-Campbell Interview – Modeling Human Drivers for Autonomous Driving

We are back with our third show this week, episode 3 of our Autonomous Vehicles Series. My guest this time is Katie Driggs-Campbell, PostDoc in the Intelligent Systems Lab at Stanford University’s Department of Aeronautics and Astronautics. Katie joins us to discuss her research into human behavioral modeling and control systems for self-driving vehicles. Katie […]

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Speaking of intelligence – DeepMind: The Podcast (Season 2, Episode 2)

Hannah explores the potential of language models, the questions they raise, and if teaching a computer about language is enough to create artificial general intelligence (AGI). Beyond helping us communicate ideas, language plays a crucial role in memory, cooperation, and thinking – which is why AI researchers have long aimed to communicate with computers using […]

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Two Minute Papers: Training Deep Neural Networks With Dropout | Two Minute Papers #62

In this episode, we discuss the bane of many machine learning algorithms – overfitting. It is also explained why it is an undesirable way to learn and how to combat it via dropout. _____________________ The paper “Dropout: A Simple Way to Prevent Neural Networks from Overtting” is available here: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Andrej Karpathy’s autoencoder is available […]

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Two Minute Papers: Overfitting and Regularization For Deep Learning | Two Minute Papers #56

In this episode, we discuss the bane of many machine learning algorithms – overfitting. It is also explained why it is an undesirable way to learn and how to combat it via L1 and L2 regularization. _____________________________ The paper “Regression Shrinkage and Selection via the Lasso” is available here: http://statweb.stanford.edu/~tibs/lasso/lasso.pdf Andrej Karpathy’s excellent lecture notes […]

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Two Minute Papers: Breaking Deep Learning Systems With Adversarial Examples | Two Minute Papers #43

Artificial neural networks are computer programs that try to approximate what the human brain does to solve problems like recognizing objects in images. In this piece of work, the authors analyze the properties of these neural networks and try to unveil what exactly makes them think that a paper towel is a paper towel, and, […]

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CoRL 2020, Spotlight Talk 184: MuGNet: Multi-Resolution Graph Neural Network for Segmenting Large…

“**MuGNet: Multi-Resolution Graph Neural Network for Segmenting Large-Scale Pointclouds** Liuyue Xie (Carnegie Mellon University)*; Tomotake Furuhata (Carnegie Mellon University); Kenji Shimada (Carnegie Mellon University) Publication: http://corlconf.github.io/paper_184/ **Abstract** In this paper, we propose a multi-resolution deep-learning architecture to segment dense large-scale pointclouds semantically. Dense pointcloud data require a computationally expensive feature encoding process before semantic segmentation. […]

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CoRL 2020, Spotlight Talk 381: Learning Object-conditioned Exploration using Distributed Soft Act…

“**Learning Object-conditioned Exploration using Distributed Soft Actor Critic** Ayzaan Wahid (Google)*; Austin C Stone (Google); Kevin Chen (Stanford); Brian Ichter (Google Brain); Alexander Toshev (Google) Publication: http://corlconf.github.io/paper_381/ **Abstract** Object navigation is defined as navigating to an object of a given label in a complex, unexplored environment. In its general form, this problem poses several challenges […]

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CoRL 2020, Spotlight Talk 45: Positive-Unlabeled Reward Learning

“**Positive-Unlabeled Reward Learning** Danfei Xu (Stanford University)*; Misha Denil (DeepMind) Publication: http://corlconf.github.io/paper_45/ **Abstract** Learning reward functions from data is a promising path towards achieving scalable Reinforcement Learning (RL) for robotics. However, a major challenge in training agents from learned reward models is that the agent can learn to exploit errors in the reward model to […]

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CoRL 2020, Spotlight Talk 459: Sampling-based Reachability Analysis: A Random Set Theory Approach…

“**Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling** Thomas Lew (Stanford University)*; Marco Pavone (Stanford University) Publication: http://corlconf.github.io/paper_459/ **Abstract** Reachability analysis is at the core of many applications, from neural network verification, to safe trajectory planning of uncertain systems. However, this problem is notoriously challenging, and current approaches tend to be either […]

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Two Minute Papers: Recurrent Neural Network Writes Sentences About Images | Two Minute Papers #23

This technique is a combination of two powerful machine learning algorithms: – convolutional neural networks are excellent at image classification, i.e., finding out what is seen on an input image, – recurrent neural networks that are capable of processing a sequence of inputs and outputs, therefore it can create sentences of what is seen on […]

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Two Minute Papers: Image Colorization With Deep Learning and Classification | Two Minute Papers #71

The paper “Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification” and its implementation are available here: http://hi.cs.waseda.ac.jp/~iizuka/projects/colorization/en/ https://github.com/satoshiiizuka/siggraph2016_colorization The video classification paper by Karpathy et al.: http://cs.stanford.edu/people/karpathy/deepvideo/ Recommended for you: Artistic Style Transfer For Videos – https://www.youtube.com/watch?v=Uxax5EKg0zA Deep Learning related Two Minute Papers […]

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CoRL 2020, Spotlight Talk 499: MATS: An Interpretable Trajectory Forecasting Representation for P…

“**MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control** Boris Ivanovic (Stanford University)*; Amine Elhafsi (Stanford University); Guy Rosman (Toyota Research Institute); Adrien Gaidon (Toyota Research Institute); Marco Pavone (Stanford University) Publication: http://corlconf.github.io/paper_499/ **Abstract** Reasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses […]

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CoRL 2020, Spotlight Talk 526: Robust Policies via Mid-Level Visual Representations: An Experimen…

**Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation** Bryan Chen (UC Berkeley)*; Alexander Sax (UC Berkeley); Francis Lewis (Stanford); Iro Armeni (Stanford University); Silvio Savarese (Stanford University); Amir Zamir (Swiss Federal Institute of Technology (EPFL)); Jitendra Malik (University of California at Berkeley); Lerrel Pinto (NYU/Berkeley) Publication: http://corlconf.github.io/paper_526/ **Abstract** Vision-based robotics […]

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CoRL 2020, Spotlight Talk 408: Safe Optimal Control Using Stochastic Barrier Functions and Deep F…

“**Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs** Marcus Pereira (Georgia Institute Technology)*; Ziyi Wang (Georgia Institute of Technology); Ioannis Exarchos (Stanford University); Evangelos Theodorou (Georgia Institute of Technology) Publication: http://corlconf.github.io/paper_408/ **Abstract** This paper introduces a new formulation for stochastic optimal control and stochastic dynamic optimization that ensures safety with respect to […]

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Two Minute Papers: This is How You Hack A Neural Network

The paper “Adversarial Reprogramming of Neural Networks” is available here: https://arxiv.org/abs/1806.11146 Andrej Karpathy’s image classifier: https://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html 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, Andrew Melnychuk, Angelos Evripiotis, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Dennis Abts, Emmanuel, Eric […]

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Two Minute Papers: This Neural Network Optimizes Itself | Two Minute Papers #212

The paper “Hierarchical Representations for Efficient Architecture Search” is available here: https://arxiv.org/pdf/1711.00436.pdf Genetic algorithm (+ Mona Lisa problem) implementation: 1. https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/ 2. https://users.cg.tuwien.ac.at/zsolnai/gfx/knapsack_genetic/ Andrej Karpathy’s online demo: http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html Overfitting and Regularization For Deep Learning – https://www.youtube.com/watch?v=6aF9sJrzxaM Training Deep Neural Networks With Dropout – https://www.youtube.com/watch?v=LhhEv1dMpKE How Do Genetic Algorithms Work? – https://www.youtube.com/watch?v=ziMHaGQJuSI We would like to […]

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CoRL 2020, Spotlight Talk 66: Reinforcement Learning with Videos: Combining Offline Observations …

“**Reinforcement Learning with Videos: Combining Offline Observations with Interaction** Karl Schmeckpeper (University of Pennsylvania)*; Oleh Rybkin (University of Pennsylvania); Kostas Daniilidis (University of Pennsylvania); Sergey Levine (UC Berkeley); Chelsea Finn (Stanford) Publication: http://corlconf.github.io/paper_66/ **Abstract** Reinforcement learning is a powerful framework for robots to acquire skills from experience, but often requires a substantial amount of online […]

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CoRL 2020, Spotlight Talk 128: Learning Latent Representations to Influence Multi-Agent Interaction

“**Learning Latent Representations to Influence Multi-Agent Interaction** Annie Xie (Stanford University)*; Dylan Losey (Stanford University); Ryan Tolsma (Stanford University); Chelsea Finn (Stanford); Dorsa Sadigh (Stanford) Publication: http://corlconf.github.io/paper_128/ **Abstract** Seamlessly interacting with humans or robots is hard because these agents are non-stationary. They update their policy in response to the ego agent’s behavior, and the ego […]

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Two Minute Papers: 10 Even Cooler Deep Learning Applications | Two Minute Papers #59

For the third time, we present another round of incredible deep learning applications! ___________________ 1. Geolocation – http://arxiv.org/abs/1602.05314 2. Super-resolution – http://arxiv.org/pdf/1511.04491v1.pdf 3. Neural Network visualizer – http://experiments.mostafa.io/public/ffbpann/ 4. Recurrent neural network for sentence completion: http://www.cs.toronto.edu/~ilya/fourth.cgi 5. Human-in-the-loop and Doctor-in-the-loop: http://link.springer.com/article/10.1007/s40708-016-0036-4 6. Emoji suggestions for images – https://emojini.curalate.com/ 7. MNIST handwritten numbers in HD – […]

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CoRL 2020 Keynote 2 – Dorsa Sadigh

Walking the Boundary of Learning and Interaction Dorsa Sadigh (Stanford) More information: https://www.robot-learning.org/program/keynotes YouTube Source for this Robot AI Video

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CoRL 2020, Spotlight Talk 189: TNT: Target-driveN Trajectory Prediction

“**TNT: Target-driveN Trajectory Prediction** Hang Zhao (Waymo)*; Jiyang Gao (Waymo); Tian Lan (Waymo); Chen Sun (Google); Ben Sapp (Waymo); Balakrishnan Varadarajan (Google Research); Yue Shen (Waymo, LLC); Yi Shen (Waymo); Yuning Chai (Waymo); Cordelia Schmid (Google); Congcong Li (Waymo); Dragomir Anguelov (Waymo) Publication: http://corlconf.github.io/paper_189/ **Abstract** Predicting the future behavior of moving agents is essential for […]

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