facebookresearch,Meta Research,github

low-shot-shrink-hallucinate

Presenting Low-shot Visual Recognition by Shrinking and Hallucinating Features

low-shot-with-diffusion

Low-shot learning with large-scale diffusion

luckmatters

Understanding Training Dynamics of Deep ReLU Networks

m-amr2text

Generate from English-Centric AMR into Multiple Languages.

m3rl

Mind-aware Multi-agent Management Reinforcement Learning

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

mae_st

Official Open Source code for "Masked Autoencoders As Spatiotemporal Learners"

many-to-many-dijkstra

A predictive model developed to identify medium-voltage electrical distribution grid infrastructure using publicly available data sources.

mask-predict

A masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation.

mask2former

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

maskformer

Per-Pixel Classification is Not All You Need for Semantic Segmentation (NeurIPS 2021, spotlight)

maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

mathsfromexamples

Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample

mbr-exec

code for "Natural Language to Code Translation with Execution"

I have implemented in both python and R two papers for estimating subgroup means under misclassification, which are useful for data analyses. T. K. MAK, W. K. LI, A new method for estimating subgroup means under misclassification, Biometrika, Volume 75, Issue 1, March 1988, Pages 105–111, https//doi.org/10.1093/biomet/75.1.105 Selén, Jan. “Adjusting for Errors in Classification and Measurement in the Analysis of Partly and Purely Categorical Data.” Journal of the American Statistical Association, vol. 81, no. 393, 1986, pp. 75–81. JSTOR, www.jstor.org/stable/2287969. Accessed 10 Aug. 2020.

mcc

Multiview Compressive Coding for 3D Reconstruction

measuring-emergent-comm

On the pitfalls of measuring emergent communication

mega

Sequence modeling with Mega.

memvit

Code Release for MeMViT Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition, CVPR 2022

mephisto

A suite of tools for managing crowdsourcing tasks from the inception through to data packaging for research use.

meshrcnn

code for Mesh R-CNN, ICCV 2019

meshtalk

Code for MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement

meta-ot

Meta Optimal Transport

meta_seq2seq

Compositional generalization through meta sequence-to-sequence learning

metabalance

MetaBalance algorithm for multi-task learning

metaicl

An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

metamulti

The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert. The repo also provides the LaTeX and BibTex sources required for replicating the paper.

metaseq

Repo for external large-scale work

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