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Yash Kumar Atri's Projects

hebbianmetalearning icon hebbianmetalearning

Meta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686

hindi-hostility-detection-constraint-2021 icon hindi-hostility-detection-constraint-2021

This repository contains the Hostility Detection dataset for Hindi language used in the shared task at Constraint@AAAI 21 workshop. For more details about the shared task please visit: https://constraint-shared-task-2021.github.io/

hw1 icon hw1

Template for CS287 HW1

hyperbolic-learning icon hyperbolic-learning

Implemented ML algorithms in hyperbolic geometry (MDS, K-Means, Support vector machines, etc.)

line-following-robot icon line-following-robot

A programme written in C language to program a microcontroller to guide a robot to follow a direction

merlot icon merlot

MERLOT: Multimodal Neural Script Knowledge Models

ml-india icon ml-india

Website for Machine Learning India (ml-india). Fostering machine learning and data science ecosystem in India.

multidoc_summarization icon multidoc_summarization

Code for the EMNLP 2018 paper "Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization"

neural-ode icon neural-ode

Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations

neutralizing-bias icon neutralizing-bias

Code and data for the paper, "Automatically Neutralizing Subjective Bias in Text"

pgo_aiml icon pgo_aiml

Default (ALICE clone) AIML set that is compatible with Program O

presumm icon presumm

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

pun-finder icon pun-finder

This model is trained over hundreds of PUNs and can do the 3 subtasks depicted in SEMEVAL 2017 Task 7

py-rouge icon py-rouge

Full Python implementation of the ROUGE metric, producing same results as in the official perl implementation.

pyreval icon pyreval

Automated Pyramid Summarization Evaluation

pytorch-hebbian icon pytorch-hebbian

A lightweight and flexible framework for Hebbian learning in PyTorch.

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