no code implementations • 9 Nov 2023 • Pragyan Banerjee, Abhinav Java, Surgan Jandial, Simra Shahid, Shaz Furniturewala, Balaji Krishnamurthy, Sumit Bhatia
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks.
no code implementations • 12 Sep 2022 • Abhinav Java, Shripad Deshmukh, Milan Aggarwal, Surgan Jandial, Mausoom Sarkar, Balaji Krishnamurthy
MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents.
no code implementations • 23 Mar 2022 • Ayush Chopra, Abhinav Java, Abhishek Singh, Vivek Sharma, Ramesh Raskar
The goal of this work is to protect sensitive information when learning from point clouds; by censoring the sensitive information before the point cloud is released for downstream tasks.
no code implementations • 2 Dec 2021 • Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
In this work, we introduce AdaSplit which enables efficiently scaling SL to low resource scenarios by reducing bandwidth consumption and improving performance across heterogeneous clients.
1 code implementation • 4 Jul 2021 • Sai Mitheran, Abhinav Java, Surya Kant Sahu, Arshad Shaikh
Session-based recommendation systems suggest relevant items to users by modeling user behavior and preferences using short-term anonymous sessions.
Ranked #5 on Session-Based Recommendations on yoochoose1/64
no code implementations • 5 Feb 2021 • Surya Kant Sahu, Abhinav Java, Arshad Shaikh, Yannic Kilcher
To that end, we first define a metric, MLH (Model Enthalpy), that measures the closeness of a set of numbers to Benford's Law and we show empirically that it is a strong predictor of Validation Accuracy.