A Collection of Variational Autoencoders (VAE) in PyTorch.
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Updated
Jun 13, 2024 - Python
A Collection of Variational Autoencoders (VAE) in PyTorch.
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Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.
A DSL for data-driven computational pipelines
Collection of popular and reproducible image denoising works.
FMA: A Dataset For Music Analysis
Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling and annotation) from WGS / targeted sequencing
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Website for NA-MIC Project Weeks
A research tool for the Iterated Prisoner's Dilemma
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
GPU-Jupyter: Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Get started DVC project (NLP, random forest)
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
Experiments for understanding disentanglement in VAE latent representations
PyCIL: A Python Toolbox for Class-Incremental Learning
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