Neural networks with linear threshold activations: structure and algorithms

15 November 2021

Papers citing "Neural networks with linear threshold activations: structure and algorithms"

12 / 12 papers shown

Title
On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth Gennadiy Averkov Christopher Hojny Maximilian Merkert 89 3 0 10 Feb 2025
On Minimal Depth in Neural Networks J. L. Valerdi 40 3 0 23 Feb 2024
Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut Hongyu Cheng Sammy Khalife Barbara Fiedorowicz Amitabh Basu 9 1 0 04 Feb 2024
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 94 32 0 29 Apr 2023
Training Neural Networks is NP-Hard in Fixed Dimension Vincent Froese Christoph Hertrich 48 4 0 29 Mar 2023
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
$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
Towards Lower Bounds on the Depth of ReLU Neural Networks Christoph Hertrich A. Basu M. D. Summa M. Skutella 34 41 0 31 May 2021
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation Christoph Hertrich Leon Sering 32 10 0 12 Feb 2021
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size Christoph Hertrich M. Skutella 50 21 0 28 May 2020
Principled Deep Neural Network Training through Linear Programming D. Bienstock Gonzalo Muñoz Sebastian Pokutta 27 24 0 07 Oct 2018
Benefits of depth in neural networks Matus Telgarsky 148 602 0 14 Feb 2016