5 papers
MIRAGE: Protecting against Malicious Image Editing via False Moderation
Anshul Nasery, Ramnath Kumar, Cho-Jui Hsieh +1
The proliferation of AI-powered image editing systems raises serious concerns because it allows personal images to be arbitrarily manipulated at scale, with minimal effort, and a l…
FastLane: Efficient Routed Systems for Late-Interaction Retrieval
Ramnath Kumar, Prateek Jain, Cho-Jui Hsieh
Late-interaction retrieval models like ColBERT achieve superior accuracy by enabling token-level interactions, but their computational cost hinders scalability and integration with…
FlexAct: Why Learn when you can Pick?
Ramnath Kumar, Kyle Ritscher, Junmin Judy +2
Learning activation functions has emerged as a promising direction in deep learning, allowing networks to adapt activation mechanisms to task-specific demands. In this work, we int…
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
Jordi Armengol-Estapé, Vincent Michalski, Ramnath Kumar +3
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross-modal learning can improve representati…
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval
Ramnath Kumar, Anshul Mittal, Nilesh Gupta +3
Dense embedding-based retrieval is widely used for semantic search and ranking. However, conventional two-stage approaches, involving contrastive embedding learning followed by app…