Search Results for author: Deepak Saini

Found 8 papers, 4 papers with code

CROSS-JEM: Accurate and Efficient Cross-encoders for Short-text Ranking Tasks

no code implementations15 Sep 2024 Bhawna Paliwal, Deepak Saini, Mudit Dhawan, Siddarth Asokan, Nagarajan Natarajan, Surbhi Aggarwal, Pankaj Malhotra, Jian Jiao, Manik Varma

In response, we propose Cross-encoders with Joint Efficient Modeling (CROSS-JEM), a novel ranking approach that enables transformer-based models to jointly score multiple items for a query, maximizing parameter utilization.

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 +6

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

1 code implementation The Web Conference 2021 Deepak Saini, Arnav Kumar Jain, Kushal Dave, Jian Jiao, Amit Singh, Ruofei Zhang and Manik Varma

An efficient end-to-end implementation of GalaXC is presented that could be trained on a dataset with 50M labels and 97M training documents in less than 100 hours on 4×V100 GPUs.

Classification Product Recommendation

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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