3 papers
cs.LG2026
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…
cs.LG2026
Differentiable Efficient Operator Search
Xiaohuan Pei, Jiyuan Zhang, Yuanfan Guo +4
Efficient multimodal foundation models often rely on manually designed token-reduction operators, such as pruning, merging, pooling, and adaptive reweighting. Although these operat…
cs.IR2026
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…