:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteur principal:	Itoh, Kei
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2409.03760
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Rethinking Deep Learning: Non-backpropagation and Non-optimization Machine Learning Approach Using Hebbian Neural Networks
par: Itoh, Kei
Publié: (2024)

Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation
par: Tang, Bo, et autres
Publié: (2026)

Learning without Global Backpropagation via Synergistic Information Distillation
par: Ye, Chenhao, et autres
Publié: (2025)

HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation
par: Dudek, Grzegorz, et autres
Publié: (2025)

Efficient Deep Learning with Decorrelated Backpropagation
par: Dalm, Sander, et autres
Publié: (2024)

Backpropagation Neural Tree
par: Ojha, Varun, et autres
Publié: (2022)

Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
par: Lucena, Anaísa, et autres
Publié: (2026)

Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machines
par: Cho, Woojin, et autres
Publié: (2026)

Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
par: Yu, Chengting, et autres
Publié: (2024)

Dynamic Spectral Backpropagation for Efficient Neural Network Training
par: Muthuraman, Mannmohan
Publié: (2025)

Optimal Control Theoretic Neural Optimizer: From Backpropagation to Dynamic Programming
par: Liu, Guan-Horng, et autres
Publié: (2025)

Stabilizing Backpropagation in 16-bit Neural Training with Modified Adam Optimizer
par: Yun, Juyoung
Publié: (2023)

Beyond Backpropagation: Exploring Innovative Algorithms for Energy-Efficient Deep Neural Network Training
par: Spyra, Przemysław
Publié: (2025)

Orthogonal Weight Modification Enhances Learning Scalability and Convergence Efficiency without Gradient Backpropagation
par: Ma, Guoqing, et autres
Publié: (2026)

Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation
par: Pöppelbaum, Johannes, et autres
Publié: (2024)

Comprehensive Survey of Complex-Valued Neural Networks: Insights into Backpropagation and Activation Functions
par: Hammad, M. M.
Publié: (2024)

Backpropagation on Dynamical Networks
par: Tan, Eugene, et autres
Publié: (2022)

Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks
par: Xu, Yichu, et autres
Publié: (2024)

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
par: Son, Hwijae
Publié: (2025)

Rethinking Structure Learning For Graph Neural Networks
par: Zheng, Yilun, et autres
Publié: (2024)

TurboBoA: Faster and Exact Attention-aware Quantization without Backpropagation
par: Kim, Junhan, et autres
Publié: (2026)

Approximate Multiplier Induced Error Propagation in Deep Neural Networks
par: Alahakoon, A. M. H. H., et autres
Publié: (2025)

Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm
par: Ghader, Mohammadnavid, et autres
Publié: (2025)

BoA: Attention-aware Post-training Quantization without Backpropagation
par: Kim, Junhan, et autres
Publié: (2024)

StatQAT: Statistical Quantizer Optimization for Deep Networks
par: Aktukmak, Mehmet, et autres
Publié: (2026)

DPZero: Private Fine-Tuning of Language Models without Backpropagation
par: Zhang, Liang, et autres
Publié: (2023)

Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML
par: Ino, Ryohei
Publié: (2024)

Rethinking Explainable Machine Learning as Applied Statistics
par: Bordt, Sebastian, et autres
Publié: (2024)

Dendritic Neural Networks with Equilibrium Propagation
par: Kubo, Yoshimasa
Publié: (2026)

Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks
par: Brenner, Rorry, et autres
Publié: (2025)

Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
par: Wang, Senmiao, et autres
Publié: (2025)

Subspace Optimization for Backpropagation-Free Continual Test-Time Adaptation
par: Sójka, Damian, et autres
Publié: (2026)

Neural Networks Learn Statistics of Increasing Complexity
par: Belrose, Nora, et autres
Publié: (2024)

The Cost of Avoiding Backpropagation
par: Panchal, Kunjal, et autres
Publié: (2025)

Rethinking Neural Network Learning Rates: A Stackelberg Perspective
par: Zeng, Sihan, et autres
Publié: (2026)

Deep Graph Learning will stall without Network Science
par: Blöcker, Christopher, et autres
Publié: (2025)

Beyond Backpropagation: Optimization with Multi-Tangent Forward Gradients
par: Flügel, Katharina, et autres
Publié: (2024)

On the Universal Statistical Consistency of Expansive Hyperbolic Deep Convolutional Neural Networks
par: Ghosh, Sagar, et autres
Publié: (2024)

GPU Memory Usage Optimization for Backward Propagation in Deep Network Training
par: Hong, Ding-Yong, et autres
Publié: (2025)

Learning Rate Optimization for Deep Neural Networks Using Lipschitz Bandits
par: Priyanka, Padma, et autres
Publié: (2024)