19 citations · 137 across the 64 of their papers we have counts for
5 papers · 1 filter
What Should World Models Forget? Stratified Retention for Continual Adaptation
Nishit Anand, Ramani Duraiswami, Dinesh Manocha
Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary prediction target, where a correct labe…
GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems
Meenakshi Krishnan, Pranav Pulijala, Ke Chen +2
Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation. Although recent geometry-adaptive neur…
Auditing Algorithmic Bias in Transformer-Based Trading
Armin Gerami, Ramani Duraiswami
Transformer models have become increasingly popular in financial applications, yet their potential risk making and biases remain under-explored. The purpose of this work is to audi…
Transformer Based Linear Attention with Optimized GPU Kernel Implementation
Armin Gerami, Ramani Duraiswami
The original softmax-based attention mechanism (regular attention) in the extremely successful Transformer architecture computes attention between tokens, each embedded in a $D…
FAST: Factorizable Attention for Speeding up Transformers
Armin Gerami, Monte Hoover, Pranav S. Dulepet +1
Motivated by the factorization inherent in the original fast multipole method and the improved fast Gauss transform we introduce a factorable form of attention that operates effici…