Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
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Updated
Jun 5, 2024 - Python
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
Deep probabilistic analysis of single-cell and spatial omics data
Learn Generative AI with PyTorch (Manning Publications, 2024)
Manifold learning for single-cell single-nucleotide genetic variations
The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback"
A GenAI app to generate hand-written characters
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
A Python package housing a collection of deep-learning multi-modal data fusion method pipelines! From data loading, to training, to evaluation - fusilli's got you covered 🌸
Exploring the depths of generative learning with a $\beta$-Variational Autoencoder ($\beta$-VAE) applied to the MNIST dataset for robust digit reconstruction and latent space analysis.
Variational Inference for Cell Type Evolution
A deep generative modeling architecture for designing lattice constrained materials
Collection of operational time series ML models and tools
Unofficial Pytorch Implementation of the Nouveau Variational AutoEncoder (NVAE) paper.
Experiments with fuzzy layers and neural nerworks
scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics
A Supervised VAE Based Gen Model for Human Motion
ResNet-style Autoencoders: Implementing and training AEs, VAEs, and CVAEs on provided dataset with TSNE visualizations.
Variational Auto Encoder
👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
Reconstructing Spatiotemporal Data with C-VAEs
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