Baran-phys's Stars
annalena-k/presentations-posters-and-other-fun-things
List of my Presentations, Posters, and other fun things
jmtomczak/intro_dgm
"Deep Generative Modeling": Introductory Examples
danpovey/conditional-flow-matching
hamrel-cxu/SPCI-code
Official implementation of the ICML 2023 work "Sequential Predictive Conformal Inference"
j991222/ai4math-papers
AI for Mathematics (AI4Math) paper list
elad-amrani/self-classifier
PyTorch implementation of "Self-Supervised Classification Network" from ECCV 2022
mcleish7/arithmetic
Code to reproduce "Transformers Can Do Arithmetic with the Right Embeddings", McLeish et al (2024)
Wickstrom/RELAX
Code for RELAX, a framework for explaining representations.
ml-stat-Sustech/TorchCP
A Python toolbox for conformal prediction research on deep learning models, using PyTorch.
probabilists/lampe
Likelihood-free AMortized Posterior Estimation with PyTorch
thoglu/jammy_flows
A package to describe amortized (conditional) normalizing-flow PDFs defined jointly on tensor products of manifolds with coverage control. The connection between different manifolds is fixed via an autoregressive structure.
deep-symbolic-mathematics/Multimodal-Math-Pretraining
[ICLR 2024 Spotlight] This is the official code for the paper "SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training"
sw-gong/GNN-Tutorial
Graph Neural Network Tutorial
recursionpharma/gflownet
GFlowNet library specialized for graph & molecular data
divelab/DIG
A library for graph deep learning research
valeman/Transformers_And_LLM_Are_What_You_Dont_Need
The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.
agracyk2/Ricci-flow-guided-autoencoders-for-dynamics
We present a manifold-based autoencoder method for learning time-dependent dynamics, namely PDEs, with a manifold latent space subject to Ricci flow.
qym7/SparseDiff
Official implementation for 'Sparse denoising diffusion for large graph generation'
JHLew/Learnable-Fourier-Features
Unofficial pytorch implementation of the paper "Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding", NeurIPS 2021.
willGuimont/learnable_fourier_positional_encoding
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding
rmcelreath/VLEGT
Verty short course on evolutionary game theory
jctops/understanding-oversquashing
OllieBoyne/sslap
Auction Algorithm for Sparse Linear Assignment Problems
lucidrains/point-transformer-pytorch
Implementation of the Point Transformer layer, in Pytorch
iml-wg/HEPML-LivingReview
Living Review of Machine Learning for Particle Physics
jwcarr/mantel
Python implementation of the Mantel test, a significance test of the correlation between two distance matrices
KingJamesSong/PDETraversal
ICML23 "Latent Traversals in Generative Models as Potential Flows"
lupalab/ExNODE
Exchangeable Neural ODE for Set Modeling
DiffEqML/torchdyn
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
maximelucas/HOI_shape_sync_differently
Code associated with the paper: "Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes," by Y. Zhang, M. Lucas, and F. Battiston.