Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes

24 February 2023

Papers citing "Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes"

16 / 16 papers shown

Title
On the Depth of Monotone ReLU Neural Networks and ICNNs Egor Bakaev Florestan Brunck Christoph Hertrich Daniel Reichman Amir Yehudayoff 11 0 0 09 May 2025
On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth Gennadiy Averkov Christopher Hojny Maximilian Merkert 78 3 0 10 Feb 2025
Neural Networks and (Virtual) Extended Formulations Christoph Hertrich Georg Loho 59 3 0 05 Nov 2024
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization Holger Boche Vít Fojtík Adalbert Fono Gitta Kutyniok 16 0 0 12 Aug 2024
On Minimal Depth in Neural Networks J. L. Valerdi 20 3 0 23 Feb 2024
Defining Neural Network Architecture through Polytope Structures of Dataset Sangmin Lee Abbas Mammadov Jong Chul Ye 43 0 0 04 Feb 2024
Extracting Formulae in Many-Valued Logic from Deep Neural Networks Yani Zhang Helmut Bölcskei 6 0 0 22 Jan 2024
Topological Expressivity of ReLU Neural Networks Ekin Ergen Moritz Grillo 43 2 0 17 Oct 2023
How Many Neurons Does it Take to Approximate the Maximum? Itay Safran Daniel Reichman Paul Valiant 23 8 0 18 Jul 2023
Neural Polytopes Koji Hashimoto T. Naito Hisashi Naito 14 1 0 03 Jul 2023
Training Neural Networks is NP-Hard in Fixed Dimension Vincent Froese Christoph Hertrich 36 4 0 29 Mar 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 40 30 0 04 Apr 2022
Towards Lower Bounds on the Depth of ReLU Neural Networks Christoph Hertrich A. Basu M. D. Summa M. Skutella 24 31 0 31 May 2021
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation Christoph Hertrich Leon Sering 13 10 0 12 Feb 2021
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size Christoph Hertrich M. Skutella 38 12 0 28 May 2020
Benefits of depth in neural networks Matus Telgarsky 121 600 0 14 Feb 2016