38 citations · 81 across the 27 of their papers we have counts for
9 papers · 1 filter
Expressivity of Quadratic Neural ODEs
Joshua Hanson, Maxim Raginsky
This work focuses on deriving quantitative approximation error bounds for neural ordinary differential equations having at most quadratic nonlinearities in the dynamics. The simple…
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
Alan Yang, Jie Xiong, Maxim Raginsky +1
This paper proposes a class of neural ordinary differential equations parametrized by provably input-to-state stable continuous-time recurrent neural networks. The model dynamics a…
Information-theoretic generalization bounds for black-box learning algorithms
Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg +1
We derive information-theoretic generalization bounds for supervised learning algorithms based on the information contained in predictions rather than in the output of the training…
Model-Augmented Estimation of Conditional Mutual Information for Feature Selection
Alan Yang, AmirEmad Ghassami, Maxim Raginsky +2
Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independ…
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
Joshua Hanson, Maxim Raginsky
There has been a recent shift in sequence-to-sequence modeling from recurrent network architectures to convolutional network architectures due to computational advantages in traini…
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
Belinda Tzen, Maxim Raginsky
In deep latent Gaussian models, the latent variable is generated by a time-inhomogeneous Markov chain, where at each time step we pass the current state through a parametric nonlin…