divyat09

divyat09's Stars

• microsoft/FLAML

  A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

  Language:Jupyter Notebook3.9k59517512

• thuml/Transfer-Learning-Library

  Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

  Language:Python3.4k49202559

• facebookresearch/higher

  higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

  Language:Python1.6k28108123

• ystael/chicago-ug-math-bib

  Chicago undergraduate mathematics bibliography

  Language:HTML69133559

• fulifeng/Causal_Reading_Group

  We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

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• microsoft/causica

  Language:Python442134155

• facebookresearch/PUG

  This is the repository for the Photorealistic Unreal Graphics (PUG) datasets for representation learning.

  Language:Jupyter Notebook2307212

• brendel-group/cl-ica

  Code for the paper "Contrastive Learning Inverts the Data Generating Process".

  Language:Python887210

• ilkhem/icebeem

  Code for ICE-BeeM paper - NeurIPS 2020

  Language:Python872518

• MaheepChaudhary/Causality-in-Trustworthy-Machine-Learning

  The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.

  Language:Python84818

• facebookresearch/RCDM

  Visualizing representations with diffusion based conditional generative model.

  Language:Python837312

• KunalMGupta/NeuralMeshFlow

  Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows

  Language:Python834712

• amit-sharma/chatgpt-causality-pairs

  Solving the causality pairs challenge (does A cause B) with ChatGPT

  Language:Python75228

• slachapelle/dcdi

  Repository for "Differentiable Causal Discovery from Interventional Data"

  Language:Python724013

• facebookresearch/disentangling-correlated-factors

  A benchmarking suite for disentanglement algorithms, suited for evaluating robustness to correlated factors. Codebase for the paper "Disentanglement of Correlated Factors via Hausdorff Factorized Support" by Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt.

  Language:Python69407

• bradyneal/realcause

  Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causal structure.

  Language:Python682117

• addtt/object-centric-library

  Library for the training and evaluation of object-centric models (ICML 2022)

  Language:Python65344

• larslorch/avici

  Amortized Inference for Causal Structure Learning, NeurIPS 2022

  Language:Python55247

• MadryLab/BREEDS-Benchmarks

  Language:Jupyter Notebook55727

• ecreager/eiil

  Code for Environment Inference for Invariant Learning (ICML 2021 Paper)

  Language:Jupyter Notebook49319

• phlippe/CITRIS

  Code repository of the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" and "iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects"

  Language:Python49787

• ysharma1126/ssl_identifiability

  Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

  Language:Python48246

• gemoran/sparse-vae-code

  Language:Jupyter Notebook32333

• ilkhem/iVAE

  VAEs and nonlinear ICA: a unifying framework

  Language:Python30323

• arkilpatel/Compositional-Generalization-Seq2Seq

  ACL 2022: Revisiting the Compositional Generalization Abilities of Neural Sequence Models

  Language:Python11200

• christinaheinze/core

  TensorFlow implementation of 'Core', proposed in "Conditional Variance Penalties and Domain Shift Robustness".

  Language:Python10502

• uhlerlab/discrepancy_vae

  code for paper: Identifiability Guarantees for Causal Disentanglement from Soft Interventions

  Language:Jupyter Notebook10202

• Genentech/nodags-flows

  Language:Jupyter Notebook9521

• AliciaCurth/CATESelection

  Sklearn-style implementations of model selection criteria for CATE estimation

  Language:Python7201

• mizunt1/structure_learning

  Language:Python6301