Variational Autoencoders (VAEs) are a type of generative model that extends traditional autoencoders by adding a probabilistic spin to their latent space representation.
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Updated
Jun 28, 2024 - Jupyter Notebook
Variational Autoencoders (VAEs) are a type of generative model that extends traditional autoencoders by adding a probabilistic spin to their latent space representation.
Collection of operational time series ML models and tools
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
Deep probabilistic analysis of single-cell and spatial omics data
Max MSP patch with Python backend to create soundscapes that match an emotion as you explore the emotion-space
Public repository for Unsupervised Binary Variational Auto-Encoder (BVAE) for Hashing
Public repository of our works in Exoplanet analysis with Deep Learning
Multimodal Pretraining for Unsupervised Protein Representation Learning
Sobolev alignment of deep probabilistic models for comparing single cell profiles
A simulation of wake behind cylinder. dimensionality reduction by variational auto encoder
Implementing Bayesian neural networks to close the amortization gap in VAEs in pytorch.
End-to-end analysis of spatial multi-omics data
Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE
Conditional normalizing flows (NFs), conditional GANs, and conditional variational autoencoders (CVAEs) with sklearn-like interface
MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
Manifold learning for single-cell single-nucleotide genetic variations
Using neural networks to build an expressive hierarchical distribution; A variational inference method to accurately estimate the posterior uncertainty; A fast and general method for approximate Bayesian inference. (ICML 2018)
VAEs with PyTorch + Lightning
A GenAI app to generate hand-written characters
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