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Neural PDE Solvers for Irregular Domains
Biswajit Khara, Ethan Herron, Zhanhong Jiang +8
Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention. However, the large majority of neural PDE solvers only a…
Active Learning for Single Neuron Models with Lipschitz Non-Linearities
Aarshvi Gajjar, Chinmay Hegde, Christopher Musco
We consider the problem of active learning for single neuron models, also sometimes called ``ridge functions'', in the agnostic setting (under adversarial label noise). Such models…
Caption supervision enables robust learners
Benjamin Feuer, Ameya Joshi, Chinmay Hegde
Vision language (VL) models like CLIP are robust to natural distribution shifts, in part because CLIP learns on unstructured data using a technique called caption supervision; the…
GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response
Govind Mittal, Chinmay Hegde, Nasir Memon
With the rise of AI-enabled Real-Time Deepfakes (RTDFs), the integrity of online video interactions has become a growing concern. RTDFs have now made it feasible to replace an impo…
Distributed Online Non-convex Optimization with Composite Regret
Zhanhong Jiang, Aditya Balu, Xian Yeow Lee +3
Regret has been widely adopted as the metric of choice for evaluating the performance of online optimization algorithms for distributed, multi-agent systems. However, data/model va…
On The Computational Complexity of Self-Attention
Feyza Duman Keles, Pruthuvi Mahesakya Wijewardena, Chinmay Hegde
Transformer architectures have led to remarkable progress in many state-of-art applications. However, despite their successes, modern transformers rely on the self-attention mechan…