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cs.LG2025
Autoencoding Dynamics: Topological Limitations and Capabilities
Matthew D. Kvalheim, Eduardo D. Sontag
Given a "data manifold" and "latent space" , an autoencoder is a pair of continuous maps consisting of an "encoder" $E\colon \mathbb{R}^n\t…
cs.LG2025
Some remarks on gradient dominance and LQR policy optimization
Eduardo D. Sontag
Solutions of optimization problems, including policy optimization in reinforcement learning, typically rely upon some variant of gradient descent. There has been much recent work i…
cs.LG2023
Why should autoencoders work?
Matthew D. Kvalheim, Eduardo D. Sontag
Deep neural network autoencoders are routinely used computationally for model reduction. They allow recognizing the intrinsic dimension of data that lie in a -dimensional subset…