Deep probabilistic analysis of single-cell and spatial omics data

A GenAI app to generate hand-written characters

This GitHub repository showcases my bachelor thesis which is focused on exploring the application and comparison of various deep generative models for synthetic image augmentation in manufacturing domain.

Implementing Bayesian neural networks to minimize the amortization gap in VAEs, investigating their potential to approximate the optimal solution to the amortization interpolation problem in PyTorch.

Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.

Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman

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 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.