Tight Hardness Results for Training Depth-2 ReLU Networks

27 November 2020

Papers citing "Tight Hardness Results for Training Depth-2 ReLU Networks"

7 / 7 papers shown

Title
Complexity of Neural Network Training and ETR: Extensions with Effectively Continuous Functions Teemu Hankala Miika Hannula J. Kontinen Jonni Virtema 19 6 0 19 May 2023
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 91 32 0 29 Apr 2023
Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes Christian Haase Christoph Hertrich Georg Loho 26 21 0 24 Feb 2023
$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
Neural networks with linear threshold activations: structure and algorithms Sammy Khalife Hongyu Cheng A. Basu 34 14 0 15 Nov 2021
Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks Tolga Ergen Mert Pilanci 24 16 0 18 Oct 2021
From Local Pseudorandom Generators to Hardness of Learning Amit Daniely Gal Vardi 109 30 0 20 Jan 2021