e-stack

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    Watch Meta Research Mirrors/self_defeating_improvements

    Code implementing the experiments of "Fixes That Fail Self-Defeating Improvements in Machine-Learning Systems".

    最近更新: 接近3年前

    Watch Meta Research Mirrors/deepmeg-recurrent-encoder

    deepmeg recurrent encoder

    最近更新: 接近3年前

    Watch Meta Research Mirrors/OnlineAttacks

    Study of a hypothetical attack scenario where an attacker corrupts images that are fed into an AI system, and has only a few chances before risking being detected.

    最近更新: 接近3年前

    Watch Meta Research Mirrors/social-catalysts

    We introduce the notion of a social catalyst in the context of social media a person who prompts interaction between their friends through their posts. We find not only that this activity is heavily skewed, but that it also correlates with participation in groups and events. We validate the behavioral measure using a survey wherein people nominate social catalysts in their friend network. Whether a person will be nominated as a social catalyst can be predicted using network features and the tendency of their posts to prompt discussion. Although individuals' role as social catalysts has not attracted much study to date, this paper demonstrates that it can be studied both via behavioral and survey measures.

    最近更新: 接近3年前

    Watch Meta Research Mirrors/fisher_information_loss

    This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

    最近更新: 接近3年前

    Watch Meta Research Mirrors/diffq

    DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

    最近更新: 接近3年前

    Watch Meta Research Mirrors/augmentation-corruption

    This repository provides code for "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness".

    最近更新: 接近3年前

    Watch Meta Research Mirrors/fews

    Experiment code to create the dataset presented in the EACL2021 paper "FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary".

    最近更新: 接近3年前

    Watch Meta Research Mirrors/soundspaces-challenge

    Starter code for SoundSpaces challenge at CVPR 21's Embodied AI workshop

    最近更新: 接近3年前

    Watch Meta Research Mirrors/unbiased-teacher

    PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

    最近更新: 接近3年前

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