3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.NE2025
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…