Pratyusha Sharma (pratyushasharma)

pratyushasharma

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Location:Cambridge, MA

Home Page:https://pratyushasharma.github.io/

Twitter:@pratyusha_PS

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Pratyusha Sharma's starred repositories

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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pytorch-CycleGAN-and-pix2pix

Image-to-Image Translation in PyTorch

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vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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pix2pix

Image-to-image translation with conditional adversarial nets

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sonnet

TensorFlow-based neural network library

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vid2vid

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

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arxiv-latex-cleaner

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

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pytorch-maml-rl

Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

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Recycle-GAN

Unsupervised Video Retargeting (e.g. face to face, flower to flower, clouds and winds, sunrise and sunset)

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how-to-read-pytorch

Quick, visual, principled introduction to pytorch code through five colab notebooks.

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alfred

ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Question-Answering

TensorFlow implementation of Match-LSTM and Answer pointer for the popular SQuAD dataset.

pytorch_Squeezenet

Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

TypeNet

A Hierarchical Type system for fine grained entity typing

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Hierarchical-Typing

Code and Data for all experiments from our ACL 2018 paper "Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking"

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ExpBERT

Code for our ACL '20 paper "Representation Engineering with Natural Language Explanations"

structural-grokking

Code for our ACL '23 paper titled "Grokking of Hierarchical Structure in Vanilla Transformers"

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TreeProjections

Tool to measure tree-structuredness of the internal algorithm learnt by a transformer

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Natural-Language-Processing

Course projects for NLP (COL772) - Spring 2016

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