Name: Vivek Talwar
Type: User
Company: International Institute of Information Technology, Hyderabad.
Bio: Research Interests : Multimodal Learning, Federated Learning, Continual Learning,
Mechanistic Interpretability.
distributed systems+ML+hpc acceleration
Twitter: gpubrr042
Location: Hyderabad, India
Vivek Talwar's Projects
InterpretDL: Interpretation of Deep Learning Models,基于『飞桨』的模型可解释性算法库。
Introduction to sleep scoring
🏅 Collection of Kaggle Solutions and Ideas 🏅
The Tensorflow, Keras implementation of Swin-Transformer and Swin-UNET
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Code for all experiments.
Implementation of Linformer for Pytorch
Scrapping complete detail from Linkedin about a person as its URL of the profile as input
Linux kernel source tree
This is an official pytorch implementation of Lite-HRNet: A Lightweight High-Resolution Network.
Using Natural Lnaguage Processing to do Sentiment Analysis of Annual Reports of Companies
VAE, Variational Autoencoder, Deep Learning, Medical Imaging
Open source platform for the machine learning lifecycle
mlpack: a scalable C++ machine learning library --
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)
AI Toolkit for Healthcare Imaging
Federated Learning Utilities and Tools for Experimentation
Official code accompanying the arXiv paper Compressing Multisets with Large Alphabets
Machine Learning on RaspberryPi with Cloud computation done on Amazon Web Services
Neural Distributed Image Compression using Common Information (NDIC) [DCC 2022]
Network Analysis in Python
Fast and Easy Infinite Neural Networks in Python
Code for "Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans" CVPR 2021 best paper candidate
nGraph has moved to OpenVINO
Neural Networks: Zero to Hero
NNGeometry is a PyTorch library for computing Fisher Information Matrices and Neural Tangent Kernels
https://opencatalystproject.org/
OmniXAI: A Library for eXplainable AI