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cs.LG2023
An Adaptive Tangent Feature Perspective of Neural Networks
Daniel LeJeune, Sina Alemohammad
In order to better understand feature learning in neural networks, we propose a framework for understanding linear models in tangent feature space where the features are allowed to…
cs.LG2023
Self-Consuming Generative Models Go MAD
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi +5
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this…
cs.LG2019
Thresholding Graph Bandits with GrAPL
Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk
In this paper, we introduce a new online decision making paradigm that we call Thresholding Graph Bandits. The main goal is to efficiently identify a subset of arms in a multi-arme…