124 citations · 218 across the 20 of their papers we have counts for
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Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and Hölder Smoothness
Misbah Uz Zaman, Anirbit Mukherjee
Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practi…
Towards a Foundation Model for the Martian Atmosphere
Sujit Roy, Udayshankar Nair, Yuling Wu +16
The martian atmosphere hosts dynamical phenomena ranging from planet-encircling dust storms to mesoscale orographic clouds and nocturnal low-level jets. General circulation model s…
Convergent Stochastic Training of Multi-Headed Attention and Understanding LoRA
Zhengkai Sun, Dibyakanti Kumar, Alejandro F Frangi +2
Transformers have revolutionized machine learning and deploying attention layers in the model is increasingly standard across a myriad of applications. Further, for large models, i…
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
Sebastien Andre-Sloan, Dibyakanti Kumar, Alejandro F Frangi +1
This work establishes rigorous first-of-its-kind upper bounds on the generalization error for the method of approximating solutions to the (d+1)-dimensional incompressible Navier-S…