Flexible Inference for Predictive Coding Networks in JAX.
[ICML 2026] PyTorch implementation of the EBiEOT method
Official implementation of our NeurIPS 2025 poster paper "PID-controlled Langevin Dynamics for Faster Sampling of Generative Models"
AI agent memory using Modern Hopfield Networks — no LLM calls, no database, one matrix multiply. MCP server for Cursor, Claude Code, and other AI coding agents.
Tracing the links between Statistical Mechanics and AI. Phase 1 features a vectorized 2D Ising Model simulation. Phase 2 maps these dynamics to Hopfield Networks to show how physical energy minimization drives memory recall.
Multimodal LLM hallucination quantification via KL-smoothed scores + spectral/energy models (RKHS, hypergraphs).
A PyTorch framework for learning expressive Energy-Based Model (EBM) priors for text generation. Replaces standard Gaussian VAE priors using Contrastive Divergence and Langevin MCMC.
Experimental validation workspace for Extropic THRML: thermodynamic computing with JAX-accelerated block Gibbs sampling
A comprehensive collection of implementations for Deep Generative Models, including VAEs, Normalizing Flows, CycleGAN, EBMs, Score-Based Models, Diffusion (DDPM/DDIM), and Flow Matching. Features applications in Anomaly Detection, Disentanglement, Style Transfer, Subject-Driven Generation (DreamBooth), and Financial Time Series Synthesis.
A research-focused modular generative modeling library built on JAX/Flax NNX
Benchmarks for TorchEBM
A unification hypothesis for intelligence. Fuzzy-to-canon compiler framework.
A post-connectionist blueprint for persistent, autopoietic AGI. This framework redefines intelligence as dynamical self-stabilization, moving from "AI as a tool" to "AI as a persistent entity" through recursive self-modeling, active inference, and non-equilibrium thermodynamics.
RailMind — Dissipative Neural Architecture. Emergent structure through energy competition.
[ICLR 2026] ``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
Interactive JEPA, Energy-Based Learning & Cognitive World Model Laboratory. 6 deep modules exploring LeCun's vision for world models.
🤖 Notes, assignments & projects for CS-844 Generative Deep Models — NUST CEME. Topics: Autoregressive Models, VAEs, GANs, Diffusion, Score-Based Models, LLMs & CLIP. Built with Python · PyTorch.
A generalized reinforcement learning framework for structured action representations and adaptive decision-making in evolving systems.
My master's thesis focused on teaching robots from a raw visual input.
Equilibrium Propagation in PyTorch: train a net without backprop via two-phase energy relaxation. Tutorial + MNIST; companion to arXiv:2601.18710.
Add a description, image, and links to the energy-based-models topic page so that developers can more easily learn about it.
To associate your repository with the energy-based-models topic, visit your repo's landing page and select "manage topics."