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    deep dive


    RLPrompt: Optimizing discrete text prompts with reinforcement learning
    We propose an efficient discrete prompt optimization approach with reinforcement learning.
    07 March 2023, by

    Fully autonomous real-world reinforcement learning with applications to mobile manipulation
    A system that learns to clean up a room directly with a real robot via continual learning.
    07 February 2023, by

    Riemannian score-based generative modelling
    The winners of a NeurIPS 2022 best paper award write about their work on generative modelling.
    01 February 2023, by

    Bottom-up top-down detection transformers for open vocabulary object detection
    We introduce a model that detects all objects that a phrase mentions.
    23 January 2023, by

    Causal confounds in sequential decision making
    Using techniques from causal inference, we derive provably correct and scalable algorithms for sequential decision making in certain settings.
    06 December 2022, by

    Tackling diverse tasks with neural architecture search
    We developed a Neural Architecture Search method that generates and trains task-specific convolutional neural networks.
    24 October 2022, by

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    reported elsewhere (by AITopics)
    ›   Google's AI chatbot Bard seems boring compared to ChatGPT and Microsoft's BingGPT - Vox ›   A US Agency Rejected Face Recognition--and Landed in Big Trouble ›   Synthetic data for AI outperform real data in robot-assisted surgery ›   China's Answer to ChatGPT Flubs Its First Lines ›   Young Sudan inventor utilises electronic waste to build robots – Middle East Monitor ›   Slip Robotics launches new trailer pallet unloading solution - The Robot Report ›   Artificial intelligence helps solve networking problems ›   Is solid-state LiDAR the key to autonomous driving? ›   Fourier Transformations Reveal How AI Learns Complex Physics ›   ChatGPT AI is now an app on my Apple Watch - but GPT4 is already on the way




    Tracking any pixel in a video
    We propose Persistent Independent Particles (PIPs), a new particle video method to track pixels in a video.
    17 October 2022, by

    Keeping learning-based control safe by regulating distributional shift
    We propose a new framework to reason about the safety of a learning-based controller with respect to its training distribution.
    30 September 2022, by

    Recurrent model-free RL can be a strong baseline for many POMDPs
    Considering an approach for dealing with realistic problems with noise and incomplete information.
    23 September 2022, by

    Reverse engineering the NTK: towards first-principles architecture design
    We propose a paradigm for bringing some principle to the art of architecture design.
    12 September 2022, by

    Galaxies on graph neural networks
    Using Graph Neural Networks, we trained Generative Adversarial Networks to correctly predict the coherent orientations of galaxies in a state-of-the-art cosmological simulation.
    05 September 2022, by

    auton-survival: An open-source package for regression, counterfactual estimation, evaluation and phenotyping censored time-to-event data
    We present auton-survival – a comprehensive Python code repository of user-friendly, machine learning tools for working with censored time-to-event data.
    22 August 2022, by

    Why do policy gradient methods work so well in cooperative MARL? Evidence from policy representation
    We show how policy gradient methods can converge to an optimal policy in certain cases, and that they can learn multi-modal policies.
    15 August 2022, by

    Does AutoML work for diverse tasks?
    Can the available AutoML tools quickly and painlessly attain near-expert performance on diverse learning tasks?
    01 August 2022, by

    FIGS: Attaining XGBoost-level performance with the interpretability and speed of CART
    In this blog post we cover a new method for fitting an interpretable model that takes the form of a sum of trees.
    12 July 2022, by

    Deep attentive variational inference
    The expressivity of current deep probabilistic models can be improved by selectively prioritizing statistical dependencies between latent variables that are potentially distant from each other.
    24 June 2022, by

    Rethinking human-in-the-loop for artificial augmented intelligence
    How do we build and evaluate an AI system for real-world applications?
    17 June 2022, by

    Bootstrapped meta-learning – an interview with Sebastian Flennerhag
    ICLR2022 award winner tells us about how he and co-authors approached the meta-learning problem.
    07 June 2022, by

    Designing societally beneficial reinforcement learning systems
    Studying the risks associated with using reinforcement learning for real-world applications.
    31 May 2022, by

    An experimental design perspective on model-based reinforcement learning
    We propose a simple algorithm that is able to solve a wide variety of control tasks.
    19 May 2022, by

    Should I use offline RL or imitation learning?
    In this blog post, we aim to understand if, when and why offline RL is a better approach for tackling a variety of sequential decision-making problems.
    17 May 2022, by

    Offline RL made easier: no TD learning, advantage reweighting, or transformers
    We try to identify the essential elements of offline RL via supervised learning.
    03 May 2022, by

    Unsupervised skill discovery with contrastive intrinsic control
    Unsupervised reinforcement learning (RL), where RL agents pre-train with self-supervised rewards, is an emerging paradigm for developing RL agents that are capable of generalization.
    01 April 2022, by

    Assessing generalization of SGD via disagreement
    We demonstrate that a simple procedure can accurately estimate the generalization error with only unlabeled data.
    21 March 2022, by

    imodels: leveraging the unreasonable effectiveness of rules
    imodels provides a simple unified interface and implementation for many state-of-the-art interpretable modeling techniques, particularly rule-based methods.
    14 March 2022, by

    Why spectral normalization stabilizes GANs: analysis and improvements
    We investigate the training stability of generative adversarial networks (GANs).
    07 March 2022, by

    The unsupervised reinforcement learning benchmark
    We consider the unsupervised RL problem - how do we learn useful behaviors without supervision and then adapt them to solve downstream tasks quickly?
    14 February 2022, by

    Improving RL with lookahead: learning off-policy with online planning
    We suggest using a policy that looks ahead using a learned model to find the best action sequence.
    11 February 2022, by

    Sequence modeling solutions for reinforcement learning problems
    We tackle large-scale reinforcement learning problems with the toolbox of sequence modeling.
    03 February 2022, by

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

     
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