103 citations · 166 across the 6 of their papers we have counts for
7 papers
Special Lagrangian cones in Deep Learning
Tejas Kotwal, Govind Menon
We introduce a matrix generalization of the cone of Harvey and Lawson and prove that it is an exact special Lagrangian manifold. We further show that it belongs to a family of exac…
On the Entropy Formula for Real, Complex, and Quaternionic Deep Linear Networks
Luis Contreras, Marco Nahas, Tejas Kotwal
We extend the entropy formula of Menon and Yu for the real Deep Linear Network (DLN) to its complex and quaternionic analogues, obtaining a unified formula for DLNs over $\mathbb{R…
From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
Saket Tiwari, Tejas Kotwal, George Konidaris
We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on…
Entropic Regularization in the Deep Linear Network
Alan Chen, Tejas Kotwal, Govind Menon
We study regularization for the deep linear network (DLN) using the entropy formula introduced in arXiv:2509.09088. The equilibria and gradient flow of the free energy on the Riema…
Active topolectrical circuits
Tejas Kotwal, Fischer Moseley, Alexander Stegmaier +6
The transfer of topological concepts from the quantum world to classical mechanical and electronic systems has opened fundamentally new approaches to protected information transmis…
Connecting the Kuramoto Model and the Chimera State
Tejas Kotwal, Xin Jiang, Daniel M. Abrams
Since its discovery in 2002, the chimera state has frequently been described as a counter-intuitive, puzzling phenomenon. The Kuramoto model, in contrast, has become a celebrated p…
Further insights into the damping-induced self-recovery phenomenon
Tejas Kotwal, Roshail Gerard, Ravi Banavar
In a series of papers, D. E. Chang, et al., proved and experimentally demonstrated a phenomenon they termed "damping-induced self-recovery". However, these papers left a few questi…