Exploiting Subgradient Sparsity in Max-Plus Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Enaieh, Ikhlas, Fercoq, Olivier |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the explainability of max-plus neural networks
by: Enaieh, Ikhlas, et al.
Published: (2026)
by: Enaieh, Ikhlas, et al.
Published: (2026)
Investigating Sparsity in Recurrent Neural Networks
by: Darji, Harshil
Published: (2024)
by: Darji, Harshil
Published: (2024)
Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL
by: Miahi, Erfan, et al.
Published: (2026)
by: Miahi, Erfan, et al.
Published: (2026)
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
by: Szatkowski, Filip, et al.
Published: (2023)
by: Szatkowski, Filip, et al.
Published: (2023)
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
by: Moakhar, Arshia Soltani, et al.
Published: (2023)
by: Moakhar, Arshia Soltani, et al.
Published: (2023)
Deep Neural Network Initialization with Sparsity Inducing Activations
by: Price, Ilan, et al.
Published: (2024)
by: Price, Ilan, et al.
Published: (2024)
UltraLIF: Fully Differentiable Spiking Neural Networks via Ultradiscretization and Max-Plus Algebra
by: Miñoza, Jose Marie Antonio
Published: (2026)
by: Miñoza, Jose Marie Antonio
Published: (2026)
Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors
by: Shehab, Mohamad Louai, et al.
Published: (2025)
by: Shehab, Mohamad Louai, et al.
Published: (2025)
Sparsity-Aware Communication for Distributed Graph Neural Network Training
by: Mukhodopadhyay, Ujjaini, et al.
Published: (2025)
by: Mukhodopadhyay, Ujjaini, et al.
Published: (2025)
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
by: Xue, Anton, et al.
Published: (2022)
by: Xue, Anton, et al.
Published: (2022)
Chordal Sparsity for SDP-based Neural Network Verification
by: Xue, Anton, et al.
Published: (2022)
by: Xue, Anton, et al.
Published: (2022)
DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion
by: Wu, Yizhuo, et al.
Published: (2025)
by: Wu, Yizhuo, et al.
Published: (2025)
SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction
by: Wu, Junfeng, et al.
Published: (2025)
by: Wu, Junfeng, et al.
Published: (2025)
On the Complexity of Finding Small Subgradients in Nonsmooth Optimization
by: Kornowski, Guy, et al.
Published: (2022)
by: Kornowski, Guy, et al.
Published: (2022)
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
by: Zhuang, Dingyi, et al.
Published: (2024)
by: Zhuang, Dingyi, et al.
Published: (2024)
Fitted Q-Iteration via Max-Plus-Linear Approximation
by: Liu, Y., et al.
Published: (2024)
by: Liu, Y., et al.
Published: (2024)
Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs
by: Ferguson, Aidan, et al.
Published: (2025)
by: Ferguson, Aidan, et al.
Published: (2025)
EXION: Exploiting Inter- and Intra-Iteration Output Sparsity for Diffusion Models
by: Heo, Jaehoon, et al.
Published: (2025)
by: Heo, Jaehoon, et al.
Published: (2025)
A De-singularity Subgradient Approach for the Extended Weber Location Problem
by: Lai, Zhao-Rong, et al.
Published: (2024)
by: Lai, Zhao-Rong, et al.
Published: (2024)
Untangling Lariats: Subgradient Following of Variationally Penalized Objectives
by: Mo, Kai-Chia, et al.
Published: (2024)
by: Mo, Kai-Chia, et al.
Published: (2024)
Exploiting Chaotic Dynamics as Deep Neural Networks
by: Liu, Shuhong, et al.
Published: (2024)
by: Liu, Shuhong, et al.
Published: (2024)
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
by: Li, Shuangjie, et al.
Published: (2024)
by: Li, Shuangjie, et al.
Published: (2024)
Rethinking the Relationship between Recurrent and Non-Recurrent Neural Networks: A Study in Sparsity
by: Hershey, Quincy, et al.
Published: (2024)
by: Hershey, Quincy, et al.
Published: (2024)
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
by: Pierro, Alessandro, et al.
Published: (2025)
by: Pierro, Alessandro, et al.
Published: (2025)
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
by: Li, Xin-Chun, et al.
Published: (2024)
by: Li, Xin-Chun, et al.
Published: (2024)
Exploiting the Structure of Two Graphs with Graph Neural Networks
by: Tenorio, Victor M., et al.
Published: (2024)
by: Tenorio, Victor M., et al.
Published: (2024)
Learning Weighted Finite Automata over the Max-Plus Semiring and its Termination
by: Okudono, Takamasa, et al.
Published: (2024)
by: Okudono, Takamasa, et al.
Published: (2024)
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration
by: Huang, Ning-Chi, et al.
Published: (2024)
by: Huang, Ning-Chi, et al.
Published: (2024)
Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity
by: Li, Xiao, et al.
Published: (2023)
by: Li, Xiao, et al.
Published: (2023)
The Stochastic Conjugate Subgradient Algorithm For Kernel Support Vector Machines
by: Zhang, Di, et al.
Published: (2024)
by: Zhang, Di, et al.
Published: (2024)
HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems
by: Chen, Qiyue, et al.
Published: (2025)
by: Chen, Qiyue, et al.
Published: (2025)
Proximal gradient descent on the smoothed duality gap to solve saddle point problems
by: Fercoq, Olivier
Published: (2025)
by: Fercoq, Olivier
Published: (2025)
Defining Lyapunov functions as the solution of a performance estimation saddle point problem
by: Fercoq, Olivier
Published: (2024)
by: Fercoq, Olivier
Published: (2024)
Monitoring the Convergence Speed of PDHG to Find Better Primal and Dual Step Sizes
by: Fercoq, Olivier
Published: (2024)
by: Fercoq, Olivier
Published: (2024)
Bayesian Neural Networks: A Min-Max Game Framework
by: Hong, Junping, et al.
Published: (2023)
by: Hong, Junping, et al.
Published: (2023)
CSF: Fixed-outline Floorplanning Based on the Conjugate Subgradient Algorithm Assisted by Q-Learning
by: Meng, Xinyan, et al.
Published: (2025)
by: Meng, Xinyan, et al.
Published: (2025)
VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration
by: Helal, Shereef, et al.
Published: (2025)
by: Helal, Shereef, et al.
Published: (2025)
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization
by: Grimmer, Benjamin, et al.
Published: (2023)
by: Grimmer, Benjamin, et al.
Published: (2023)
Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
by: Zhang, Siyuan, et al.
Published: (2024)
by: Zhang, Siyuan, et al.
Published: (2024)
Sparsity Forcing: Reinforcing Token Sparsity of MLLMs
by: Chen, Feng, et al.
Published: (2025)
by: Chen, Feng, et al.
Published: (2025)
Similar Items
-
On the explainability of max-plus neural networks
by: Enaieh, Ikhlas, et al.
Published: (2026) -
Investigating Sparsity in Recurrent Neural Networks
by: Darji, Harshil
Published: (2024) -
Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL
by: Miahi, Erfan, et al.
Published: (2026) -
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
by: Szatkowski, Filip, et al.
Published: (2023) -
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
by: Moakhar, Arshia Soltani, et al.
Published: (2023)