benhuh Goto Github PK

ai-feynman

arc

The Abstraction and Reasoning Corpus

backpack

bias_free_denoising

Robust and Interpretable Blind Image Denoising via Bias-Free Convolutional Neural Networks

bibs

Annotated bibliographies.

bindsnet

Simulation of spiking neural networks (SNNs) using PyTorch.

bispectral-networks

Official PyTorch implementation of Bispectral Neural Networks, ICLR 2023

blockdrop

BlockDrop: Dynamic Inference Paths in Residual Networks

brain-tokyo-workshop

🧠🗼

capsule-networks

A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

cavia

Code for "Fast Context Adaptation via Meta-Learning"

deep_attentive_vi

Author's implementation of the paper: "Deep Attentive Variational Inference", ICLR 2022.

deeptensorfactorization4grouprep

deepweightdecay

delay_autoencoder

dpr

Code for Deep Single-image Portrait Image Relighting

efficient-vdvae

Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

glom

Forked from Agglomerator

hfopt-matlab

Modified version of hfopt-matlab by David Sussillo.

hypernetworks

PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet (Residual Networks)

matrix-capsules-em-pytorch

A PyTorch Implementation of Matrix Capsules with EM Routing

ml-ista

Demo for Multi-Layer ISTA and Multi-Layer FISTA algorithms for convolutional neural networks, as described in J. Sulam, A. Aberdam, A. Beck, M. Elad, (2018). On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. arXiv preprint:1806.00701

mri

n2nmn

Code release for Hu et al. Learning to Reason: End-to-End Module Networks for Visual Question Answering. in ICCV, 2017

nn2pp

Utilities to train two-layer neural networks

norse

A library to do deep learning with spiking neural networks.

open-l2o

Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms

phaseharmonics

Wavelet phase harmonic scattering transform

pytorch-lightning

The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate

pytorch-maml-rl

Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch