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math.DG2026
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…
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…