When Deep Learning Meets Polyhedral Theory: A Survey

29 April 2023

Papers citing "When Deep Learning Meets Polyhedral Theory: A Survey"

27 / 27 papers shown

Title
Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks Nandi Schoots M. Villani Niels uit de Bos 56 1 0 03 Mar 2025
Reinforcement learning with combinatorial actions for coupled restless bandits Lily Xu Bryan Wilder Elias B. Khalil Milind Tambe 41 1 0 01 Mar 2025
Neural Networks and (Virtual) Extended Formulations Christoph Hertrich Georg Loho 42 1 0 05 Nov 2024
Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations Pablo Carrasco Gonzalo Muñoz 26 1 0 30 Oct 2024
Certified Robustness to Data Poisoning in Gradient-Based Training Philip Sosnin Mark N. Müller Maximilian Baader Calvin Tsay Matthew Wicker AAML SILM 26 1 0 09 Jun 2024
A rank decomposition for the topological classification of neural representations Kosio Beshkov Gaute T. Einevoll 40 1 0 30 Apr 2024
The Real Tropical Geometry of Neural Networks Marie-Charlotte Brandenburg Georg Loho Guido Montúfar 24 1 0 18 Mar 2024
Parallel Algorithms for Exact Enumeration of Deep Neural Network Activation Regions Sabrina Drammis Bowen Zheng Karthik Srinivasan R. Berwick Nancy A. Lynch R. Ajemian 29 1 0 29 Feb 2024
Defining Neural Network Architecture through Polytope Structures of Dataset Sangmin Lee Abbas Mammadov Jong Chul Ye 18 1 0 04 Feb 2024
Generating Likely Counterfactuals Using Sum-Product Networks Jiri Nemecek Tomás Pevný Jakub Marecek TPM 35 1 0 25 Jan 2024
Optimization Over Trained Neural Networks: Taking a Relaxing Walk Jiatai Tong Junyang Cai Thiago Serra 32 2 0 07 Jan 2024
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks Fabian Badilla Marcos Goycoolea Gonzalo Muñoz Thiago Serra 29 6 0 27 Dec 2023
PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs Mark Turner Antonia Chmiela Thorsten Koch Michael Winkler AI4CE 26 1 0 13 Dec 2023
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design Tom McDonald Calvin Tsay Artur M. Schweidtmann Neil Yorke-Smith 30 4 0 02 Dec 2023
Topological Expressivity of ReLU Neural Networks Ekin Ergen Moritz Grillo 20 1 0 17 Oct 2023
Deep ReLU Networks Have Surprisingly Simple Polytopes Fenglei Fan Wei Huang Xiang-yu Zhong Lecheng Ruan T. Zeng Huan Xiong Fei-Yue Wang 35 3 0 16 May 2023
Training Neural Networks is NP-Hard in Fixed Dimension Vincent Froese Christoph Hertrich 23 1 0 29 Mar 2023
Globally Optimal Training of Neural Networks with Threshold Activation Functions Tolga Ergen Halil Ibrahim Gulluk Jonathan Lacotte Mert Pilanci 38 8 0 06 Mar 2023
Effects of Data Geometry in Early Deep Learning Saket Tiwari G. Konidaris 28 6 0 29 Dec 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 18 1 0 04 Apr 2022
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints Jan Kronqvist Ruth Misener Calvin Tsay 16 1 0 10 Feb 2022
Mixed-Integer Optimization with Constraint Learning Donato Maragno H. Wiberg Dimitris Bertsimas Ş. Birbil D. Hertog Adejuyigbe O. Fajemisin 32 38 0 04 Nov 2021
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment Alyssa Kody Samuel C. Chevalier Spyros Chatzivasileiadis Daniel Molzahn 26 30 0 21 Oct 2021
On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity Yuuki Takai Akiyoshi Sannai Matthieu Cordonnier 42 3 0 23 Oct 2020
Reachability Analysis for Feed-Forward Neural Networks using Face Lattices Xiaodong Yang Hoang-Dung Tran Weiming Xiang Taylor Johnson CVBM 47 18 0 02 Mar 2020
CAQL: Continuous Action Q-Learning Moonkyung Ryu Yinlam Chow Ross Anderson Christian Tjandraatmadja Craig Boutilier 173 41 0 26 Sep 2019
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 194 1,714 0 03 Feb 2017