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    deep dive


    An energy-based perspective on learning observation models
    We propose a conceptually novel approach to mapping sensor readings into states.
    21 January 2022, by

    Which mutual information representation learning objectives are sufficient for control?
    How can we best design representation learning objectives?
    10 January 2022, by

    Understanding user interfaces with screen parsing
    We introduce the problem of screen parsing, which we use to predict structured user interface models from visual information.
    03 January 2022, by

    Bridge data: boosting generalization of robotic skills with cross-domain datasets
    With our proposed dataset and multi-task, multi-domain learning approach, we have shown one potential avenue for making diverse datasets reusable in robotics.
    30 December 2021, by

    Why generalization in RL is difficult: epistemic POMDPs and implicit partial observability
    In this blog post, we will aim to explain why generalization in RL is fundamentally hard, even in theory.
    21 December 2021, by

    Designs from data: offline black-box optimization via conservative training
    In this post, we discuss offline model-based optimization and some recent advances in this area.
    10 December 2021, by

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    ›   Now You Can Rent a Robot Worker--for Less Than Paying a Human ›   MMTs edge closer to artificial intelligence networks ›   Former Google scientist says the computers that run our lives exploit us -- and he has a way to stop them ›   Physics-Based Engineering and the Machine-Learning "Black Box" Problem ›   Social distancing surveillance: how robots will keep you in line ›   Portable laser scanner creates colour 3D images of surfaces or objects ›   Robot piloted by a ball of algae is powered by photosynthesis ›   California reviews whether Tesla's self-driving tests require oversight ›   Algorithm Helps Robots Avoid Obstacles in Their Path ›   Companies Using AI to Identify Small Molecules to Treat Parkinson's




    A first-principles theory of neural network generalization
    Find out more about research trying to shed light on the workings of deep neural networks.
    22 November 2021, by

    Compression, transduction, and creation: a unified framework for evaluating natural language generation
    Our framework classifies language generation tasks into compression, transduction, and creation.
    18 November 2021, by

    Making RL tractable by learning more informative reward functions: example-based control, meta-learning, and normalized maximum likelihood
    This article presents MURAL, a method for learning uncertainty-aware rewards for RL.
    15 November 2021, by

    PICO: Pragmatic compression for human-in-the-loop decision-making
    In this post, we outline a pragmatic compression algorithm called PICO.
    05 November 2021, by

    Unsolved ML safety problems
    We provide a new roadmap for ML Safety and aim to refine the technical problems that the field needs to address.
    26 October 2021, by

    Distilling neural networks into wavelet models using interpretations
    We propose a method which distills information from a trained DNN into a wavelet transform.
    18 October 2021, by

    What can I do here? Learning new skills by imagining visual affordances
    Can we use an analogous strategy to a child learning in a robotic learning system?
    30 September 2021, by

    The limitations of limited context for constituency parsing
    We consider the representational power of two important frameworks for constituency parsing.
    24 September 2021, by

    Strategic instrumental variable regression: recovering causal relationships from strategic responses
    How can we infer causal relationships in data while employing a decision-making model?
    08 September 2021, by

    Universal weakly supervised segmentation by pixel-to-segment contrastive learning
    Can a machine learn from a few labeled pixels to predict every pixel in a new image?
    17 August 2021, by

    A unifying, game-theoretic framework for imitation learning
    In this post we explore imitation learning and consider online, offline, and interactive methods.
    13 August 2021, by

    The surprising effectiveness of PPO in cooperative multi-agent games
    A study of cooperative multi-agent tasks, in which a group of agents is trying to optimize a shared reward function.
    06 August 2021, by




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