Provably Good Solutions to the Knapsack Problem via Neural Networks of
Bounded Size

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Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size

28 May 2020

Christoph Hertrich

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Papers citing "Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size"

9 / 9 papers shown

Title
Neural Networks and (Virtual) Extended Formulations Christoph Hertrich Georg Loho 196 3 0 05 Nov 2024
All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformers Amira Hijazi Osman Ozaltin Reha Uzsoy 94 0 0 21 Oct 2024
Positional Attention: Expressivity and Learnability of Algorithmic Computation George Giapitzakis Artur Back de Luca Shenghao Yang Petar Veličković Kimon Fountoulakis 216 3 0 02 Oct 2024
Unsupervised Extractive Summarization with Learnable Length Control Strategies Renlong Jie Xiaojun Meng Xin Jiang Qun Liu 121 4 0 12 Dec 2023
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework Mitchell Keegan Mahdi Abolghasemi 116 1 0 06 Dec 2023
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods Constantine Caramanis Dimitris Fotakis Alkis Kalavasis Vasilis Kontonis Christos Tzamos 126 5 0 08 Oct 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 207 32 0 04 Apr 2022
Towards Lower Bounds on the Depth of ReLU Neural Networks Christoph Hertrich A. Basu M. D. Summa M. Skutella 214 48 0 31 May 2021
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation Christoph Hertrich Leon Sering 142 10 0 12 Feb 2021