69 citations · 217 across the 26 of their papers we have counts for
4 papers · 1 filter
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks
Eshaan Nichani, Adityanarayanan Radhakrishnan, Caroline Uhler
Recent works have demonstrated that increasing model capacity through width in over-parameterized neural networks leads to a decrease in test risk. For neural networks, however, mo…
Linear Convergence of Generalized Mirror Descent with Time-Dependent Mirrors
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
The Polyak-Lojasiewicz (PL) inequality is a sufficient condition for establishing linear convergence of gradient descent, even in non-convex settings. While several recent works us…
Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
Anastasiya Belyaeva, Louis Cammarata, Adityanarayanan Radhakrishnan +4
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and exper…
On Alignment in Deep Linear Neural Networks
Adityanarayanan Radhakrishnan, Eshaan Nichani, Daniel Bernstein +1
We study the properties of alignment, a form of implicit regularization, in linear neural networks under gradient descent. We define alignment for fully connected networks with mul…