Assessing Simplification Levels in Neural Networks: The Impact of Hyperparameter Configurations on Complexity and Sensitivity
Fuente:
arXiv
Saved in:
| Main Author: | Guan, Huixin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
by: Adkins, Jacob, et al.
Published: (2024)
by: Adkins, Jacob, et al.
Published: (2024)
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
by: Onorato, Gabriele
Published: (2024)
by: Onorato, Gabriele
Published: (2024)
Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks
by: Li, Kevin, et al.
Published: (2024)
by: Li, Kevin, et al.
Published: (2024)
Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
by: Saegert, Paul, et al.
Published: (2026)
by: Saegert, Paul, et al.
Published: (2026)
Set-Valued Sensitivity Analysis of Deep Neural Networks
by: Wang, Xin, et al.
Published: (2024)
by: Wang, Xin, et al.
Published: (2024)
Complex Physics-Informed Neural Network
by: Si, Chenhao, et al.
Published: (2025)
by: Si, Chenhao, et al.
Published: (2025)
Understanding the Mechanisms of Fast Hyperparameter Transfer
by: Ghosh, Nikhil, et al.
Published: (2025)
by: Ghosh, Nikhil, et al.
Published: (2025)
ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
by: Schröder, Maresa, et al.
Published: (2026)
by: Schröder, Maresa, et al.
Published: (2026)
Evaluating Simplification Algorithms for Interpretability of Time Series Classification
by: Håvardstun, Brigt, et al.
Published: (2025)
by: Håvardstun, Brigt, et al.
Published: (2025)
Parallelizing Node-Level Explainability in Graph Neural Networks
by: Llorente, Oscar, et al.
Published: (2026)
by: Llorente, Oscar, et al.
Published: (2026)
Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency
by: Ammar, Adel, et al.
Published: (2025)
by: Ammar, Adel, et al.
Published: (2025)
Combining Automated Optimisation of Hyperparameters and Reward Shape
by: Dierkes, Julian, et al.
Published: (2024)
by: Dierkes, Julian, et al.
Published: (2024)
Calibrated Dataset Condensation for Faster Hyperparameter Search
by: Ding, Mucong, et al.
Published: (2024)
by: Ding, Mucong, et al.
Published: (2024)
Using Large Language Models for Hyperparameter Optimization
by: Zhang, Michael R., et al.
Published: (2023)
by: Zhang, Michael R., et al.
Published: (2023)
Hyperparameters in Score-Based Membership Inference Attacks
by: Pradhan, Gauri, et al.
Published: (2025)
by: Pradhan, Gauri, et al.
Published: (2025)
Hyperparameter Optimization via Interacting with Probabilistic Circuits
by: Seng, Jonas, et al.
Published: (2025)
by: Seng, Jonas, et al.
Published: (2025)
Sequential Policy Gradient for Adaptive Hyperparameter Optimization
by: Li, Zheng, et al.
Published: (2025)
by: Li, Zheng, et al.
Published: (2025)
Deep Neural Networks via Complex Network Theory: a Perspective
by: La Malfa, Emanuele, et al.
Published: (2024)
by: La Malfa, Emanuele, et al.
Published: (2024)
FSX: Message Flow Sensitivity Enhanced Structural Explainer for Graph Neural Networks
by: Feng, Bizu, et al.
Published: (2026)
by: Feng, Bizu, et al.
Published: (2026)
On the Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacy
by: Pereira, Luiz, et al.
Published: (2026)
by: Pereira, Luiz, et al.
Published: (2026)
Stochastic Weight Sharing for Bayesian Neural Networks
by: Lin, Moule, et al.
Published: (2025)
by: Lin, Moule, et al.
Published: (2025)
Kernel Stochastic Configuration Networks for Nonlinear Regression
by: Chen, Yongxuan, et al.
Published: (2024)
by: Chen, Yongxuan, et al.
Published: (2024)
Recurrent Stochastic Configuration Networks with Incremental Blocks
by: Dang, Gang, et al.
Published: (2024)
by: Dang, Gang, et al.
Published: (2024)
Hyperparameter Importance Analysis for Multi-Objective AutoML
by: Theodorakopoulos, Daphne, et al.
Published: (2024)
by: Theodorakopoulos, Daphne, et al.
Published: (2024)
Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport
by: Amad, Harry, et al.
Published: (2026)
by: Amad, Harry, et al.
Published: (2026)
ReplaceMe: Network Simplification via Depth Pruning and Transformer Block Linearization
by: Shopkhoev, Dmitriy, et al.
Published: (2025)
by: Shopkhoev, Dmitriy, et al.
Published: (2025)
Efficient and Interpretable Neural Networks Using Complex Lehmer Transform
by: Ataei, Masoud, et al.
Published: (2025)
by: Ataei, Masoud, et al.
Published: (2025)
On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis
by: Guan, Junyi, et al.
Published: (2025)
by: Guan, Junyi, et al.
Published: (2025)
Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications
by: Halfon, Alon, et al.
Published: (2024)
by: Halfon, Alon, et al.
Published: (2024)
A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization
by: Zhang, Jiazhao, et al.
Published: (2024)
by: Zhang, Jiazhao, et al.
Published: (2024)
HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization
by: Wever, Marcel, et al.
Published: (2025)
by: Wever, Marcel, et al.
Published: (2025)
Beware of the Batch Size: Hyperparameter Bias in Evaluating LoRA
by: Lee, Sangyoon, et al.
Published: (2026)
by: Lee, Sangyoon, et al.
Published: (2026)
Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
by: Carstensen, Timur, et al.
Published: (2025)
by: Carstensen, Timur, et al.
Published: (2025)
Graph Unlearning: Efficient Node Removal in Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2025)
by: Guan, Faqian, et al.
Published: (2025)
Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity
by: Abuduweili, Abulikemu, et al.
Published: (2024)
by: Abuduweili, Abulikemu, et al.
Published: (2024)
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks
by: Jiang, Jingchi, et al.
Published: (2024)
by: Jiang, Jingchi, et al.
Published: (2024)
Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network
by: Piroti, Shirwan, et al.
Published: (2024)
by: Piroti, Shirwan, et al.
Published: (2024)
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
by: Ding, Mucong, et al.
Published: (2024)
by: Ding, Mucong, et al.
Published: (2024)
TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks
by: Papillon, Mathilde, et al.
Published: (2024)
by: Papillon, Mathilde, et al.
Published: (2024)
Perturbing the Phase: Analyzing Adversarial Robustness of Complex-Valued Neural Networks
by: Eilers, Florian, et al.
Published: (2026)
by: Eilers, Florian, et al.
Published: (2026)
Similar Items
-
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
by: Adkins, Jacob, et al.
Published: (2024) -
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
by: Onorato, Gabriele
Published: (2024) -
Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks
by: Li, Kevin, et al.
Published: (2024) -
Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
by: Saegert, Paul, et al.
Published: (2026) -
Set-Valued Sensitivity Analysis of Deep Neural Networks
by: Wang, Xin, et al.
Published: (2024)