Mrinal Mathur (mrinal18)

mrinal18

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Mrinal Mathur's repositories

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denoising-diffusion-pytorch

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

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mrinal18.github.io

Personal website

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wedding-website

Our Wedding Website đź‘«

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KG-LLM

experimenting on LLMs

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TAP-3DMoGen

3d motion generation conditioned on both text and audio prompts

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ubisoft-laforge-ZeroEGGS

All about ZeroEggs

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nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs.

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openai-cookbook

Examples and guides for using the OpenAI API

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deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

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machine-learning-specialization-andrew-ng

A collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization.

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ImageBind

ImageBind One Embedding Space to Bind Them All

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2nd_place_GISLR

2nd Place Google - Isolated Sign Language Recognition

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DREAMPlace

Deep learning toolkit-enabled VLSI placement

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AgentGPT

🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.

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Grounded-Segment-Anything

Marrying Grounding DINO with Segment Anything & Stable Diffusion & BLIP & Whisper - Automatically Detect , Segment and Generate Anything with Image, Text, and Speech Inputs

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Deep-Reinforcement-Learning-Tutorial

This repository is to add all the content for RL that I am currently learning and experimenting with

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Auto-GPT

An experimental open-source attempt to make GPT-4 fully autonomous.

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the-algorithm

Source code for Twitter's Recommendation Algorithm

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the-algorithm-ml

Source code for Twitter's Recommendation Algorithm

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OCT_Super_Resolution

Digital Image Processing Project

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gnn

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

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NFL_Kaggle

The National Football League (NFL) and Amazon Web Services (AWS)teamed up to develop the best sports injury surveillance and mitigation programvia the Kaggle competition. Through which the NFL wants to assign specificplayers to each helmet, which would help accurately identify each player’s “ex-posures” throughout a football play. We are trying to implement a computer vi-sion based ML algorithms capable of assigning detected helmet impacts to correctplayers via tracking information. Our paper will explain the approach to automati-cally track player helmets and their collisions. This will also allow them to reviewprevious plays and explore the trends in exposure over time.

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wav2seq

Official code for Wav2Seq

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