Learning Deep ReLU Networks Is Fixed-Parameter Tractable

28 September 2020

Papers citing "Learning Deep ReLU Networks Is Fixed-Parameter Tractable"

9 / 9 papers shown

Title
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks Brendon G. Anderson Samuel Pfrommer Somayeh Sojoudi OOD 24 1 0 07 Oct 2023
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials Ilias Diakonikolas D. Kane 24 4 0 24 Jul 2023
A faster and simpler algorithm for learning shallow networks Sitan Chen Shyam Narayanan 33 7 0 24 Jul 2023
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 91 32 0 29 Apr 2023
Operator theory, kernels, and Feedforward Neural Networks P. Jorgensen Myung-Sin Song James Tian 32 0 0 03 Jan 2023
Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis Alexander Munteanu Simon Omlor Zhao-quan Song David P. Woodruff 27 15 0 26 Jun 2022
$Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete$ Training Fully Connected Neural Networks is $\exists\mathbb{R}$ -Complete Daniel Bertschinger Christoph Hertrich Paul Jungeblut Tillmann Miltzow Simon Weber OffRL 54 30 0 04 Apr 2022
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks Sitan Chen Aravind Gollakota Adam R. Klivans Raghu Meka 21 30 0 10 Feb 2022
Efficiently Learning Any One Hidden Layer ReLU Network From Queries Sitan Chen Adam R. Klivans Raghu Meka MLAU MLT 42 8 0 08 Nov 2021