no code implementations • 15 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.
1 code implementation • 18 Aug 2024 • Jatin Prakash, Anirudh Buvanesh, Bishal Santra, Deepak Saini, Sachin Yadav, Jian Jiao, Yashoteja Prabhu, Amit Sharma, Manik Varma
Extreme Classification (XC) aims to map a query to the most relevant documents from a very large document set.
no code implementations • 28 Feb 2024 • Anshul Mittal, Shikhar Mohan, Deepak Saini, Siddarth Asokan, Suchith C. Prabhu, Lakshya Kumar, Pankaj Malhotra, Jain jiao, Amit Singh, 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.
no code implementations • 10 Jul 2022 • Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, Deepesh Hada, Vidit Jain, Bhawna Paliwal, Anshul Mittal, Sonu Mehta, Ramachandran Ramjee, Sumeet Agarwal, Purushottam Kar, Manik Varma
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
1 code implementation • 12 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.
1 code implementation • 1 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.
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.
no code implementations • 25 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.