Size and depth of monotone neural networks: interpolation and
approximation

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Size and depth of monotone neural networks: interpolation and approximation

12 July 2022

Daniel Reichman

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Papers citing "Size and depth of monotone neural networks: interpolation and approximation"

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Title
Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Davide Sartor Alberto Sinigaglia Gian Antonio Susto 204 0 0 05 May 2025
Neural Networks and (Virtual) Extended Formulations Christoph Hertrich Georg Loho 115 3 0 05 Nov 2024
Robust Reinforcement Learning with Dynamic Distortion Risk Measures Anthony Coache S. Jaimungal 95 1 0 16 Sep 2024
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks Shashank Rajput Kartik K. Sreenivasan Dimitris Papailiopoulos Amin Karbasi 36 25 0 14 Jun 2021
Certified Monotonic Neural Networks Xingchao Liu Xing Han Na Zhang Qiang Liu 70 82 0 20 Nov 2020
Counterexample-Guided Learning of Monotonic Neural Networks Aishwarya Sivaraman G. Farnadi T. Millstein Guy Van den Broeck 57 53 0 16 Jun 2020
Neural Networks Learning and Memorization with (almost) no Over-Parameterization Amit Daniely 69 33 0 22 Nov 2019
Error bounds for approximations with deep ReLU networks Dmitry Yarotsky 198 1,236 0 03 Oct 2016
The Power of Depth for Feedforward Neural Networks Ronen Eldan Ohad Shamir 228 732 0 12 Dec 2015
$Fast, Provable Algorithms for Isotonic Regression in all $\ell_{p}$-norms$ Fast, Provable Algorithms for Isotonic Regression in all $\ell_{p}$ -norms Rasmus Kyng Anup B. Rao Sushant Sachdeva 70 47 0 02 Jul 2015
Monotonic Calibrated Interpolated Look-Up Tables Maya R. Gupta Andrew Cotter Jan Pfeifer Konstantin Voevodski K. Canini Alexander Mangylov Wojtek Moczydlowski A. V. Esbroeck 208 129 0 23 May 2015