Search Results for author: Anshul Mittal

Found 8 papers, 4 papers with code

Graph Regularized Encoder Training for Extreme Classification

no code implementations28 Feb 2024 Anshul Mittal, Shikhar Mohan, Deepak Saini, Suchith C. Prabhu, Jain jiao, Sumeet Agarwal, Soumen Chakrabarti, Purushottam Kar, Manik Varma

The paper notices that in these settings, it is much more effective to use graph data to regularize encoder training than to implement a GCN.

Classification TAG

EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval

no code implementations13 Oct 2023 Ramnath Kumar, Anshul Mittal, Nilesh Gupta, Aditya Kusupati, Inderjit Dhillon, Prateek Jain

Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query.

Contrastive Learning Retrieval

Multi-modal Extreme Classification

1 code implementation CVPR 2022 Anshul Mittal, Kunal Dahiya, Shreya Malani, Janani Ramaswamy, Seba Kuruvilla, Jitendra Ajmera, Keng-hao Chang, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors.

Classification Product Recommendation

DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

1 code implementation12 Nov 2021 Kunal Dahiya, Deepak Saini, Anshul Mittal, Ankush Shaw, Kushal Dave, Akshay Soni, Himanshu Jain, Sumeet Agarwal, Manik Varma

Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set.

Multi-Label Learning

DECAF: Deep Extreme Classification with Label Features

1 code implementation1 Aug 2021 Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal, Deepak Saini, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.

Classification Extreme Multi-Label Classification +5

ECLARE: Extreme Classification with Label Graph Correlations

1 code implementation31 Jul 2021 Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.

Classification Extreme Multi-Label Classification +7

DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases

no code implementations25 Sep 2019 Kunal Dahiya, Anshul Mittal, Deepak Saini, Kushal Dave, Himanshu Jain, Sumeet Agarwal, Manik Varma

The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.

Learning Word Embeddings Multi-Label Learning +2

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