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Name: Meta Research
Type: Organization
Location: Menlo Park, California
Name: Meta Research
Type: Organization
Location: Menlo Park, California
The codes reproduce the figures and statistics in the paper, "A graphical method of cumulative differences between two subpopulations," by Mark Tygert. The repo also provides the LaTeX and BibTex sources required for replicating the paper.
The codes reproduce the figures and statistics in the paper, "Cumulative deviation of a subpopulation from the full population," by Mark Tygert. The repo also provides the LaTeX and BibTex sources required for replicating the paper.
Implements the bootstrap and jackknife methods of http://tygert.com/jdssv.pdf
Private computation framework library allows developers to perform randomized controlled trials, without leaking information about who participated or what action an individual took. It uses secure multiparty computation to guarantee this privacy. It is suitable for conducting A/B testing, or measuring advertising lift and learning the aggregate statistics without sharing information on the individual level.
FBPCP (Facebook Private Computation Platform) is a secure, privacy safe and scalable architecture to deploy MPC (Multi Party Computation) applications in a distributed way on virtual private clouds. FBPCF (Facebook Private Computation Framework) is for scaling MPC computation up via threading, while FBPCP is for scaling MPC computation out via Private Scaling architecture.
FBPCS (Facebook Private Computation Solutions) leverages secure multi-party computation (MPC) to output aggregated data without making unencrypted, readable data available to the other party or any third parties. Facebook provides impression & opportunity data, and the advertiser provides conversion / outcome data.
This is a Tensor Train based compression library to compress sparse embedding tables used in large-scale machine learning models such as recommendation and natural language processing. We showed this library can reduce the total model size by up to 100x in Facebook’s open sourced DLRM model while achieving same model quality. Our implementation is faster than the state-of-the-art implementations. Existing the state-of-the-art library also decompresses the whole embedding tables on the fly therefore they do not provide memory reduction during runtime of the training. Our library decompresses only the requested rows therefore can provide 10,000 times memory footprint reduction per embedding table. The library also includes a software cache to store a portion of the entries in the table in decompressed format for faster lookup and process.
Library for accessing capacitive measurements with the FDC1004 (TI) with Arduino.
Fluctuation-dissipation relations for stochastic gradient descent
Experiment code to create the dataset presented in the EACL2021 paper "FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary".
The paper studies the problem of learning to recognize a new class of objects from a very small number of labeled images. This is called few-shot learning. Previous work in the literature focused on designing new algorithms that allow to learn to generalize to new unseen classes.In this work, we consider the impact of the dataset that we train on, and experiment with some dataset manipulations to see which trade-offs are important in the design of a dataset aimed at few-shot learning.
FFCV-SSL Fast Forward Computer Vision for Self-Supervised Learning.
Fusion-in-Decoder
FIND: search For Inductive biases IN Deep seq2seq
Fine grained annotations extending hateful memes dataset with additional labels for identifying protected categories and attack types.
This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"
This repository reproduces the results of the paper: "Fixing the train-test resolution discrepancy" https://arxiv.org/abs/1906.06423
Fréchet Joint Distance
Federated Learning with Partial Model Personalization
Framework for writing deep learning training loops. Lightweight, and retaining full freedom to design as you see fits. It handles checkpointing, logging, distributed, compatibility with Dora, and more!
Official Open Source code for "Scaling Language-Image Pre-training via Masking"
Facebook Low Resource (FLoRes) MT Benchmark
FlowFrame enforces information flow control policies in Scala Spark applications.
Federated Learning Simulator (FLSim) is a flexible, standalone core library that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such as vision and text.
Fourier modal method with Jax
The source code to reproduce the results reported in the 'Federated Online Learning to Rank with Evolution Strategies' paper, published at WSDM 2019.
Experiment code for FPPE paper.
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
Replication code for "What Does Perception Bias on Social Networks Tell Us About Friend Count Satisfaction?", TheWebConf2022.
Define an ML problem to train with Pytorch and to leverage Pytorch's functionality for multiprocessing and distributed compute.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.