gomb0c

gomb0c's Stars

• wildphoton/Compositional-Generalization

  [NeurIPS 2022] Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language

  Language:Python132

• yoshihidesawada/DCUF

  Disentangling controllable and uncontrollable factors by Interacting with the World

  Language:Python2

• hchau630/LSC

  Code for the arXiv preprint "Disentangling images with Lie group transformations and sparse coding" (2020).

  Language:Jupyter Notebook123

• equivariant-ml/equivariant-representation-learning

  Language:Python72

• neurreps/awesome-neural-geometry

  A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond

  90960

• Bayer-Group/giae

  Implementation of the Paper "Unsupervised Learning of Group Invariant and Equivariant Representations" presented at NeurIPS 2022.

  Language:Jupyter Notebook72

• facebookresearch/DomainBed

  DomainBed is a suite to test domain generalization algorithms

  Language:Python1.4k294

• bethgelab/InDomainGeneralizationBenchmark

  Language:Python336

• ThomasMrY/Groupified-VAE

  [ICLR 2022] code for "Towards building a group-based unsupervised representation disentanglement framework"

  Language:Jupyter Notebook141

• Schlumberger/joint-vae

  Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation :star2:

  Language:Jupyter Notebook46062

• YannDubs/disentangling-vae

  Experiments for understanding disentanglement in VAE latent representations

  Language:Python785143

• clementchadebec/benchmark_VAE

  Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

  Language:Python1.8k161

• amir-abdi/disentanglement-pytorch

  Disentanglement library for PyTorch

  Language:Python27130

• IndustAI/learning-group-structure

  Code associated with our paper "Learning Group Structure and Disentangled Representations of Dynamical Environments"

  Language:Jupyter Notebook143

• stfc-sciml/dae

  Contains the code for the ICML 2023 paper, entitled "Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations"

  Language:Python41

• Zhangwenbo0324/repo_abstract_reasoning

  Language:Python1

• khalil-research/ARGA-AAAI23

  Abstract Reasoning with Graph Abstractions (ARGA) implementation

  Language:Python538

• Shanka123/STSN

  Slot Transformer Scoring Network

  Language:Python6

• husheng12345/SRAN

  Stratified Rule-Aware Network for Abstract Visual Reasoning, AAAI 2021

  Language:Python3115

• WellyZhang/RAVEN

  RAVEN: A Dataset for Relational and Analogical Visual rEasoNing

  Language:Python15029

• Fen9/WReN

  A Pytorch implementation of "Measuring abstract reasoning in neural networks" in ICML 2018 by DeepMind

  Language:Python386

• WellyZhang/PrAE

  Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution

  Language:Python242

• WellyZhang/ALANS

  Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning

  Language:Python121

• NVlabs/Bongard-LOGO

  Bongard-LOGO is a Python code repository with the purpose of generating synthetic Bongard problems on a large scale with little human intervention.

  Language:Python5110

• xukai92/bsp

  Language:Julia181

• JindongJiang/GNM

  Official Release of NeurIPS 2020 Spotlight paper "Generative Neurosymbolic Machines"

  Language:Python344

• rosinality/vq-vae-2-pytorch

  Implementation of Generating Diverse High-Fidelity Images with VQ-VAE-2 in PyTorch

  Language:Python1.6k270

• zacwellmer/WorldModels

  World Models with TensorFlow 2

  Language:Jupyter Notebook27831

• hardmaru/WorldModelsExperiments

  World Models Experiments

  Language:Jupyter Notebook609171

• ellisk42/ec

  Language:Slash454137