A community repository for benchmarking Bayesian methods

Official implementation of Diffusion Time Estimation

SOSOpt is a Python library designed for solving sums-of-squares (SOS) optimization problems.

PyTorch code for FLD (Feature Likelihood Divergence), FID, KID, Precision, Recall, etc. using DINOv2, InceptionV3, CLIP, etc.

A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.

Figure sizes, font sizes, fonts, and more configurations at minimal overhead. Fix your journal papers, conference proceedings, and other scientific publications.

A principled library for tuning, training and evaluating tabular data synthesis on fidelity, privacy and utility. CCS 2025.

Learning to Skip the Middle Layers of Transformers

NeuPI: Neural Probabilistic Inference

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

a python framework to build, learn and reason about probabilistic circuits and tensor networks

Repository of the article "Graph Conditional Flow Matching for Relational Data Generation"

CUDA kernels for generalized matrix-multiplication in PyTorch

We integrate discrete diffusion models with neurosymbolic predictors for scalable and calibrated learning and reasoning

Concept Quality Analysis

Stochastic Automatic Differentiation library for PyTorch.

Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.

[TMLR] Research code for the paper "Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling".

Stanford Drone Dataset with non-convex Constraints

gasp! - GPU Accelerated Simplical Polynomial Integrator

Official repository for "Logically Consistent Language Models via Neuro-Symbolic Integration", accepted at ICLR 2025.

Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs

CausalFlows: A library for Causal Normalizing Flows in Pytorch

Materials for the AAAI'25 tutorial "From Tensor Factorizations to Circuits (and Back)"