Lower bounds over Boolean inputs for deep neural networks with ReLU gates

8 November 2017

Papers citing "Lower bounds over Boolean inputs for deep neural networks with ReLU gates"

9 / 9 papers shown

Title
Neural Networks and (Virtual) Extended Formulations Christoph Hertrich Georg Loho 75 3 0 05 Nov 2024
Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes Christian Haase Christoph Hertrich Georg Loho 34 21 0 24 Feb 2023
Transformers Learn Shortcuts to Automata Bingbin Liu Jordan T. Ash Surbhi Goel A. Krishnamurthy Cyril Zhang OffRL LRM 46 156 0 19 Oct 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 59 30 0 04 Apr 2022
VC dimension of partially quantized neural networks in the overparametrized regime Yutong Wang Clayton D. Scott 20 1 0 06 Oct 2021
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 23 7 0 30 Jan 2021
Learning One-hidden-layer ReLU Networks via Gradient Descent Xiao Zhang Yaodong Yu Lingxiao Wang Quanquan Gu MLT 28 134 0 20 Jun 2018
Limits on representing Boolean functions by linear combinations of simple functions: thresholds, ReLUs, and low-degree polynomials Richard Ryan Williams 27 26 0 26 Feb 2018
Benefits of depth in neural networks Matus Telgarsky 148 602 0 14 Feb 2016