Atharva Kulkarni (athrvkk)

athrvkk

Geek Repo

Company:University of Southern California

Location:Los Angeles, CA

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

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Atharva Kulkarni's repositories

Flipkart-Product-Category-Classification

Implementation of ML algorithms for FlipKart Product Category Classification based on the product's description and other features.

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Anti-National-Tweets-Classification

Build a classifier to classify anti-national tweets from normal tweets based on the Khalistan movement.

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MTL-group-robustness

Official repo of the TMLR 2024 paper Multitask Learning Can Improve Worst-Group Outcomes

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python-and-machine-learning-cheatsheets

A one-stop shop for python and machine learning tips and tricks!

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UCI-Sentiment-Analysis

Implementation of various Machine Learning and Deep Learning models for Sentiment Analysis on the 'Sentiment Labelled Sentences Data Set' by University of California, Irvine.

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Book-My-Turf-Android-App

A first of its kind Android App for connecting football enthusiasts to local turfs.

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ICON-2020-TechDoFication

The repository contains team SPPU_AKAH's submission for the ICON 2020 TechDOfication Shared Task (Subtask-1f). We propose a hybrid BiLSTM-CNN based Attention Ensemble model for the task of coarse-grained automatic technical domain identification of short texts in the Marathi Language. Our system resulted in the best submission for the subtask-1f.

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Meta-Word-Embeddings

Implementation of Meta-Word-Embeddings, a combination of word2vec, GloVe, and fassttext word embeddings using various types of autoencoders.

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Semantic-Textual-Similarity

Implementation of a Siamese LSTM for Semantic Textual Similarity task.

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Algorithms-for-Sea-Route-Optimizaiton

Finding the distance-based optimal sea route using Depth First Search (DFS), Dynamic Programming (DP), and Beam Search.

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awesome-multimodal-ml

Reading list for research topics in multimodal machine learning

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mental-health-datasets

An evolving list of electronic media data sets used to model mental-health status.

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wilds

A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

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