Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
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Updated
May 25, 2024 - Jupyter Notebook
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
Design of target-focused libraries by probing continuous fingerprint space with recurrent neural networks. The repository accompanies a research paper which is currently under review (08.04.24)
Python library for solving reinforcement learning (RL) problems using generative models.
scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics
Machine learning and data analysis package implemented in JavaScript and its online demo.
CraftsMan: High-fidelity Mesh Generation with 3D Native Diffusion and Interactive Geometry Refiner
Synthetic data generation for tabular data
日本語LLMまとめ - Overview of Japanese LLMs
Creata✨allows you to create stunning AI-generated images effortlessly🖼️, it is a project that showcases my UI and API skills.
Generative modeling of synthetic time series data and time series augmentations
PyTorch implementation of PerCo (Towards Image Compression with Perfect Realism at Ultra-Low Bitrates, ICLR 2024)
Forward-backward conditional sampling
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.
This is an open collection of state-of-the-art (SOTA), novel Text to X (X can be everything) methods (papers, codes and datasets).
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Retrieval-Augmented (RAG) based pretrained GPT model that predicts and analyses the November 2024 US General Elections using news sources (CNN, FoxNews, Politico, and NPR) as context
Implementation of the paper "On the Asymptotic Mean Square Error Optimality of Diffusion Probabilistic Models."
This code sets up and trains a GAN to generate artwork images using Keras and TensorFlow, defining and compiling discriminator and generator networks, and saving results.
This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.
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