Cover Image

On the Hardness of Training Deep Neural Networks Discretely

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Doron-Arad, Ilan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Why ReLU? A Bit-Model Dichotomy for Deep Network Training
by: Doron-Arad, Ilan, et al.
Published: (2026)

Online Realizable Regression and Applications for ReLU Networks
by: Doron-Arad, Ilan, et al.
Published: (2026)

A Theory of Online Learning with Autoregressive Chain-of-Thought Reasoning
by: Doron-Arad, Ilan, et al.
Published: (2026)

Non-Linear Paging
by: Doron-Arad, Ilan, et al.
Published: (2024)

Deep Neural Network Initialization with Sparsity Inducing Activations
by: Price, Ilan, et al.
Published: (2024)

Approximations and Hardness of Packing Partially Ordered Items
by: Doron-Arad, Ilan, et al.
Published: (2024)

Concurrent Training and Layer Pruning of Deep Neural Networks
by: Guenter, Valentin Frank Ingmar, et al.
Published: (2024)

Training Deep Morphological Neural Networks as Universal Approximators
by: Fotopoulos, Konstantinos, et al.
Published: (2025)

Hardness of Learning Fixed Parities with Neural Networks
by: Shoshani, Itamar, et al.
Published: (2025)

Lossless Compression of Neural Network Components: Weights, Checkpoints, and K/V Caches in Low-Precision Formats
by: Heilper, Anat, et al.
Published: (2025)

Training Neural Networks is NP-Hard in Fixed Dimension
by: Froese, Vincent, et al.
Published: (2023)

Exact Gauss-Newton Optimization for Training Deep Neural Networks
by: Korbit, Mikalai, et al.
Published: (2024)

Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques
by: Arad, Alon, et al.
Published: (2025)

HardNet++: Nonlinear Constraint Enforcement in Neural Networks
by: Goertzen, Andrea, et al.
Published: (2026)

HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees
by: Min, Youngjae, et al.
Published: (2024)

Unsplittable Flow on a Short Path
by: Doron-Arad, Ilan, et al.
Published: (2024)

You (Almost) Can't Beat Brute Force for 3-Matroid Intersection
by: Doron-Arad, Ilan, et al.
Published: (2024)

Fine Grained Lower Bounds for Multidimensional Knapsack
by: Doron-Arad, Ilan, et al.
Published: (2024)

An EPTAS for Cardinality Constrained Multiple Knapsack via Iterative Randomized Rounding
by: Doron-Arad, Ilan, et al.
Published: (2023)

Lower Bounds for Matroid Optimization Problems with a Linear Constraint
by: Doron-Arad, Ilan, et al.
Published: (2023)

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery
by: Guenter, Valentin Frank Ingmar, et al.
Published: (2024)

Deep Neural Network Training as Random Effects: An Optimization-Inference Duality
by: Yao, Minhao, et al.
Published: (2026)

Training Memory in Deep Neural Networks: Mechanisms, Evidence, and Measurement Gaps
by: Sevetlidis, Vasileios, et al.
Published: (2026)

CPT: Efficient Deep Neural Network Training via Cyclic Precision
by: Fu, Yonggan, et al.
Published: (2021)

Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
by: Ganguly, Abhisek, et al.
Published: (2026)

DAE-HardNet: A Physics Constrained Neural Network Enforcing Differential-Algebraic Hard Constraints
by: Golder, Rahul, et al.
Published: (2025)

A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks
by: Mandal, Saptarshi, et al.
Published: (2024)

RAYEN: Imposition of Hard Convex Constraints on Neural Networks
by: Tordesillas, Jesus, et al.
Published: (2023)

HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
by: Specht, Thomas, et al.
Published: (2024)

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
by: Iftakher, Ashfaq, et al.
Published: (2025)

Hard-Label Cryptanalytic Extraction of Neural Network Models
by: Chen, Yi, et al.
Published: (2024)

Hardness of Learning Neural Networks under the Manifold Hypothesis
by: Kiani, Bobak T., et al.
Published: (2024)

PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Time
by: Pourali, Alireza, et al.
Published: (2024)

Layerwise Progressive Freezing Enables STE-Free Training of Deep Binary Neural Networks
by: Smith, Evan Gibson, et al.
Published: (2026)

SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks
by: Valipour, Mojtaba, et al.
Published: (2023)

AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks
by: Tenison, Irene, et al.
Published: (2025)

On the Dataless Training of Neural Networks
by: Velasquez, Alvaro, et al.
Published: (2025)

A Deep Generative Model for the Simulation of Discrete Karst Networks
by: Lauzon, Dany, et al.
Published: (2025)

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)

Self-Abstraction Learning for Effective and Stable Training of Deep Neural Networks
by: Cho, Wonyong, et al.
Published: (2026)