Saved in:
| Main Author: | Fujita, Kazuhisa |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.14814 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
AlphaViT: A flexible game-playing AI for multiple games and variable board sizes
by: Fujita, Kazuhisa
Published: (2024)
by: Fujita, Kazuhisa
Published: (2024)
Error Diffusion: Post Training Quantization with Block-Scaled Number Formats for Neural Networks
by: Khodamoradi, Alireza, et al.
Published: (2024)
by: Khodamoradi, Alireza, et al.
Published: (2024)
Benchmarking Neural Network Training Algorithms
by: Dahl, George E., et al.
Published: (2023)
by: Dahl, George E., et al.
Published: (2023)
Z-Error Loss for Training Neural Networks
by: Godin, Guillaume
Published: (2025)
by: Godin, Guillaume
Published: (2025)
BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training
by: Colombo, Luca, et al.
Published: (2025)
by: Colombo, Luca, et al.
Published: (2025)
IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddings
by: Nakasho, Kazuhisa
Published: (2026)
by: Nakasho, Kazuhisa
Published: (2026)
Training Large Neural Networks With Low-Dimensional Error Feedback
by: Hanut, Maher, et al.
Published: (2025)
by: Hanut, Maher, et al.
Published: (2025)
The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
by: Papamichail, Merkouris, et al.
Published: (2026)
by: Papamichail, Merkouris, et al.
Published: (2026)
BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion
by: Wei, Jia, et al.
Published: (2024)
by: Wei, Jia, et al.
Published: (2024)
A Neural Network Algorithm for KL Divergence Estimation with Quantitative Error Bounds
by: Foss, Mikil, et al.
Published: (2025)
by: Foss, Mikil, et al.
Published: (2025)
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
by: Yang, Yongyi, et al.
Published: (2024)
by: Yang, Yongyi, et al.
Published: (2024)
DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
by: Shing, Makoto, et al.
Published: (2025)
by: Shing, Makoto, et al.
Published: (2025)
Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification
by: Singh, Ruhaan, et al.
Published: (2025)
by: Singh, Ruhaan, et al.
Published: (2025)
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification
by: Alsmeier, Hendrik, et al.
Published: (2025)
by: Alsmeier, Hendrik, et al.
Published: (2025)
Training Artificial Neural Networks by Coordinate Search Algorithm
by: Rokhsatyazdi, Ehsan, et al.
Published: (2024)
by: Rokhsatyazdi, Ehsan, et al.
Published: (2024)
Towards Guided Descent: Optimization Algorithms for Training Neural Networks At Scale
by: Nagwekar, Ansh
Published: (2025)
by: Nagwekar, Ansh
Published: (2025)
Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks
by: Liu, Meitong, et al.
Published: (2026)
by: Liu, Meitong, et al.
Published: (2026)
Training Latent Diffusion Models with Interacting Particle Algorithms
by: Wang, Tim Y. J., et al.
Published: (2025)
by: Wang, Tim Y. J., et al.
Published: (2025)
Beta-Scheduling: Momentum from Critical Damping as a Diagnostic and Correction Tool for Neural Network Training
by: Pasichnyk, Ivan
Published: (2026)
by: Pasichnyk, Ivan
Published: (2026)
Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks
by: Shoham, Elad, et al.
Published: (2024)
by: Shoham, Elad, et al.
Published: (2024)
CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment
by: Mendes, Pedro, et al.
Published: (2025)
by: Mendes, Pedro, et al.
Published: (2025)
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
by: Chen, Xingran, et al.
Published: (2026)
by: Chen, Xingran, et al.
Published: (2026)
Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization
by: Li, Zexi, et al.
Published: (2024)
by: Li, Zexi, et al.
Published: (2024)
A Convexity-dependent Two-Phase Training Algorithm for Deep Neural Networks
by: Hrycej, Tomas, et al.
Published: (2025)
by: Hrycej, Tomas, et al.
Published: (2025)
Adaptive Soft Error Protection for Neural Network Processing
by: Xue, Xinghua, et al.
Published: (2024)
by: Xue, Xinghua, et al.
Published: (2024)
Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion
by: Kumar, Nishant, et al.
Published: (2023)
by: Kumar, Nishant, et al.
Published: (2023)
On the Dataless Training of Neural Networks
by: Velasquez, Alvaro, et al.
Published: (2025)
by: Velasquez, Alvaro, et al.
Published: (2025)
The Evolution of Learning Algorithms for Artificial Neural Networks
by: Baxter, Jonathan
Published: (2025)
by: Baxter, Jonathan
Published: (2025)
The Generalization Error of Supervised Machine Learning Algorithms
by: Perlaza, Samir M., et al.
Published: (2024)
by: Perlaza, Samir M., et al.
Published: (2024)
Learning to Add, Multiply, and Execute Algorithmic Instructions Exactly with Neural Networks
by: de Luca, Artur Back, et al.
Published: (2025)
by: de Luca, Artur Back, et al.
Published: (2025)
On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks
by: Su, Junwei, et al.
Published: (2025)
by: Su, Junwei, et al.
Published: (2025)
A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
by: Hossain, Forsad Al, et al.
Published: (2025)
by: Hossain, Forsad Al, et al.
Published: (2025)
Reasoning Algorithmically in Graph Neural Networks
by: Numeroso, Danilo
Published: (2024)
by: Numeroso, Danilo
Published: (2024)
Port-Hamiltonian Neural Networks with Output Error Noise Models
by: Moradi, Sarvin, et al.
Published: (2025)
by: Moradi, Sarvin, et al.
Published: (2025)
Improved Particle Approximation Error for Mean Field Neural Networks
by: Nitanda, Atsushi
Published: (2024)
by: Nitanda, Atsushi
Published: (2024)
Gradient Flow Matching for Learning Update Dynamics in Neural Network Training
by: Shou, Xiao, et al.
Published: (2025)
by: Shou, Xiao, et al.
Published: (2025)
Self-Training the Neurochaos Learning Algorithm
by: M, Anusree, et al.
Published: (2026)
by: M, Anusree, et al.
Published: (2026)
How Graph Neural Networks Learn: Lessons from Training Dynamics
by: Yang, Chenxiao, et al.
Published: (2023)
by: Yang, Chenxiao, et al.
Published: (2023)
Training Neural Samplers with Reverse Diffusive KL Divergence
by: He, Jiajun, et al.
Published: (2024)
by: He, Jiajun, et al.
Published: (2024)
Multiscale Training of Convolutional Neural Networks
by: Ahamed, Shadab, et al.
Published: (2025)
by: Ahamed, Shadab, et al.
Published: (2025)
Similar Items
-
AlphaViT: A flexible game-playing AI for multiple games and variable board sizes
by: Fujita, Kazuhisa
Published: (2024) -
Error Diffusion: Post Training Quantization with Block-Scaled Number Formats for Neural Networks
by: Khodamoradi, Alireza, et al.
Published: (2024) -
Benchmarking Neural Network Training Algorithms
by: Dahl, George E., et al.
Published: (2023) -
Z-Error Loss for Training Neural Networks
by: Godin, Guillaume
Published: (2025) -
BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training
by: Colombo, Luca, et al.
Published: (2025)