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Search results for pytorch reproducible research

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19 search results found

Pytorch Vae ⭐ 5,292

A Collection of Variational Autoencoders (VAE) in PyTorch.

Bayesian Neural Networks ⭐ 1,633

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Benchmark_vae ⭐ 1,597

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Torch Fidelity ⭐ 820

High-fidelity performance metrics for generative models in PyTorch

Disentangling Vae ⭐ 668

Experiments for understanding disentanglement in VAE latent representations

PyCIL: A Python Toolbox for Class-Incremental Learning

Labnotebook ⭐ 528

LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.

Fullsubnet ⭐ 443

PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

Lamda Pilot ⭐ 134

🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox

Hash Embeddings ⭐ 93

PyTorch implementation of Hash Embeddings (NIPS 2017). Submission to the NIPS Implementation Challenge.

Oaprogression ⭐ 58

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Variational Sparse Coding ⭐ 47

Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge

moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.

Polyaxon Quick Start ⭐ 29

A quick start project for polyaxon

Hash Grid Encoding ⭐ 24

Pure PyTorch implementation of Nvidia's hash grid encoding: https://nvlabs.github.io/instant-ngp/

Reproducibilty Challenge Ecanet ⭐ 22

Unofficial Implementation of ECANets (CVPR 2020) for the Reproducibility Challenge 2020.

Ner Lstm Cnn Pytorch ⭐ 18

End-to-end-Sequence-Labeling-via-Bi-directional-LS

PyTorch code of our ICIP'19 paper "Improving Super-Resolution Methods via Incremental Residual Learning"

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