17 citations · 30 across the 23 of their papers we have counts for
5 papers · 1 filter
Executing Causal Structure Learning with Linear-Attention Transformers
Amartya Roy, Sayar Karmakar
Transformers can execute algorithms on data given in their input. We ask whether they can do the same for causal discovery. We study a standard continuous method that repeatedly up…
Towards Size-Independent Generalization Bounds for Deep Operator Nets
Pulkit Gopalani, Sayar Karmakar, Dibyakanti Kumar +1
In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems. A particularly active area in this theme has been…
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate
Sayar Karmakar, Anirbit Mukherjee
A particular direction of recent advance about stochastic deep-learning algorithms has been about uncovering a rather mysterious heavy-tailed nature of the stationary distribution…
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm
Sayar Karmakar, Anirbit Mukherjee
In this work, we demonstrate provable guarantees on the training of a single ReLU gate in hitherto unexplored regimes. We give a simple iterative stochastic algorithm that can trai…
Depth-2 Neural Networks Under a Data-Poisoning Attack
Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou
In this work, we study the possibility of defending against data-poisoning attacks while training a shallow neural network in a regression setup. We focus on doing supervised learn…