68 Open source projects that are alternatives of or similar to rA9

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)

Experiments with spiking neural networks (SNNs) in PyTorch. See https://github.com/BINDS-LAB-UMASS/bindsnet for the successor to this project.

Synaptic Plasticity with Online Reinforcement learning

Functional models and algorithms for sparse signal processing

Neural Simulator for AGI research and development

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

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The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

One-Shot Object Appearance Learning using Spiking Neural Networks

Simulation of spiking neural networks (SNNs) using PyTorch.

Contains code for the paper "Vision Transformers are Robust Learners" (AAAI 2022).

JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"

Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

Unofficial JAX implementations of deep learning research papers

Flax is a neural network library for JAX that is designed for flexibility.

Diverse, Noisy and Parallel: a New Spiking Neural Network Approach for Humanoid Robot Control

Implementations and checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX (Flax).

RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.

Documentation:

No description or website provided.

Toy implementations of some popular ML optimizers using Python/JAX

Companion code in JAX for the paper Parallel Iterated Extended and Sigma-Point Kalman Smoothers.

👩 Pytorch and Jax code for the Madam optimiser.

Auryn: A fast simulator for spiking neural networks with synaptic plasticity

Event-Based Angular Velocity Regression with Spiking Networks

Chef Transformer 🍲 .

Python library for easy creation and running of spiking neural networks in tensorflow.

OpenN@S: Open-source software to NAS automatic VHDL code generation

Revisiting Rainbow

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Deep learning integration for Nengo

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

Papers : Biological and Artificial Neural Networks

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

Graphsignal Python agent

Trax — Deep Learning with Clear Code and Speed

Data encoders

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

Spiking neural network system

A small library for creating and manipulating custom JAX Pytree classes

A GPT, made only of MLPs, in Jax

Implementation of https://srush.github.io/annotated-s4

Brain models implementation with BrainPy

Massively parallel rigidbody physics simulation on accelerator hardware.

JAX implementations of core Deep RL algorithms

Bayesian Optimization in JAX

Liquid State Machines in Python and NEST

A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations

spiking-neural-networks

KoCLIP: Korean port of OpenAI CLIP, in Flax

Implementation of a Spiking Neural Network in Tensorflow.

Federated posterior averaging implemented in JAX

ADAM implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.

This repository will host models, modules, algorithms and applications developed by the INRC Community to run on the Intel Loihi Platform.

Brain Dynamics Programming in Python

Deep learning for spiking neural networks

Training spiking networks with hybrid ann-snn conversion and spike-based backpropagation

A brian2 extension to simulate spiking neural networks on GPUs

CARLsim is an efficient, easy-to-use, GPU-accelerated software framework for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail.