Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks
Fuente:
arXiv
Saved in:
| Main Author: | Tamamori, Akira |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Efficient event-driven retrieval in high-capacity kernel Hopfield networks
by: Tamamori, Akira
Published: (2026)
by: Tamamori, Akira
Published: (2026)
Kernel Logistic Regression Learning for High-Capacity Hopfield Networks
by: Tamamori, Akira
Published: (2025)
by: Tamamori, Akira
Published: (2025)
Quantitative Attractor Analysis of High-Capacity Kernel Hopfield Networks
by: Tamamori, Akira
Published: (2025)
by: Tamamori, Akira
Published: (2025)
Kernel Ridge Regression for Efficient Learning of High-Capacity Hopfield Networks
by: Tamamori, Akira
Published: (2025)
by: Tamamori, Akira
Published: (2025)
Self-Organization and Spectral Mechanism of Attractor Landscapes in High-Capacity Kernel Hopfield Networks
by: Tamamori, Akira
Published: (2025)
by: Tamamori, Akira
Published: (2025)
Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
by: Tamamori, Akira
Published: (2025)
by: Tamamori, Akira
Published: (2025)
Quantization robustness from dense representations of sparse functions in high-capacity kernel associative memory
by: Tamamori, Akira
Published: (2026)
by: Tamamori, Akira
Published: (2026)
Classifying States of the Hopfield Network with Improved Accuracy, Generalization, and Interpretability
by: McAlister, Hayden, et al.
Published: (2025)
by: McAlister, Hayden, et al.
Published: (2025)
Accelerating Hopfield Network Dynamics: Beyond Synchronous Updates and Forward Euler
by: Goemaere, Cédric, et al.
Published: (2023)
by: Goemaere, Cédric, et al.
Published: (2023)
A Framework for Non-Linear Attention via Modern Hopfield Networks
by: Farooq, Ahmed
Published: (2025)
by: Farooq, Ahmed
Published: (2025)
Prototype Analysis in Hopfield Networks with Hebbian Learning
by: McAlister, Hayden, et al.
Published: (2024)
by: McAlister, Hayden, et al.
Published: (2024)
Nonparametric Modern Hopfield Models
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
Random Features Hopfield Networks generalize retrieval to previously unseen examples
by: Kalaj, Silvio, et al.
Published: (2024)
by: Kalaj, Silvio, et al.
Published: (2024)
Investigating the generative dynamics of energy-based neural networks
by: Tausani, Lorenzo, et al.
Published: (2023)
by: Tausani, Lorenzo, et al.
Published: (2023)
Long-Sequence Memory with Temporal Kernels and Dense Hopfield Functionals
by: Farooq, Ahmed
Published: (2025)
by: Farooq, Ahmed
Published: (2025)
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-Mixer
by: Ota, Toshihiro, et al.
Published: (2023)
by: Ota, Toshihiro, et al.
Published: (2023)
Benchmarking local Hebbian learning rules for memory storage and prototype extraction
by: Lansner, Anders, et al.
Published: (2026)
by: Lansner, Anders, et al.
Published: (2026)
The boundary of neural network trainability is fractal
by: Sohl-Dickstein, Jascha
Published: (2024)
by: Sohl-Dickstein, Jascha
Published: (2024)
Learning Hippo: Multi-attractor Dynamics and Stability Effects in a Biologically Detailed CA3 Extension of Hopfield Networks
by: Corradetti, Daniele, et al.
Published: (2026)
by: Corradetti, Daniele, et al.
Published: (2026)
An Invariant Information Geometric Method for High-Dimensional Online Optimization
by: Zhang, Zhengfei, et al.
Published: (2024)
by: Zhang, Zhengfei, et al.
Published: (2024)
Dynamical stability for dense patterns in discrete attractor neural networks
by: Cohen, Uri, et al.
Published: (2025)
by: Cohen, Uri, et al.
Published: (2025)
Causal Explanations from the Geometric Properties of ReLU Neural Networks
by: Woods, Hector, et al.
Published: (2026)
by: Woods, Hector, et al.
Published: (2026)
Self-orthogonalizing attractor neural networks emerging from the free energy principle
by: Spisak, Tamas, et al.
Published: (2025)
by: Spisak, Tamas, et al.
Published: (2025)
Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
Universality of reservoir systems with recurrent neural networks
by: Yasumoto, Hiroki, et al.
Published: (2024)
by: Yasumoto, Hiroki, et al.
Published: (2024)
Gated recurrent neural networks discover attention
by: Zucchet, Nicolas, et al.
Published: (2023)
by: Zucchet, Nicolas, et al.
Published: (2023)
Learning fast changing slow in spiking neural networks
by: Capone, Cristiano, et al.
Published: (2024)
by: Capone, Cristiano, et al.
Published: (2024)
Designing deep neural networks for driver intention recognition
by: Vellenga, Koen, et al.
Published: (2024)
by: Vellenga, Koen, et al.
Published: (2024)
Learning richness modulates equality reasoning in neural networks
by: Tong, William L., et al.
Published: (2025)
by: Tong, William L., et al.
Published: (2025)
Emergent representations in networks trained with the Forward-Forward algorithm
by: Tosato, Niccolò, et al.
Published: (2023)
by: Tosato, Niccolò, et al.
Published: (2023)
A developmental approach for training deep belief networks
by: Zambra, Matteo, et al.
Published: (2022)
by: Zambra, Matteo, et al.
Published: (2022)
Three factor delay learning rules for spiking neural networks
by: Vassallo, Luke, et al.
Published: (2026)
by: Vassallo, Luke, et al.
Published: (2026)
Improved weight initialization for deep and narrow feedforward neural network
by: Lee, Hyunwoo, et al.
Published: (2023)
by: Lee, Hyunwoo, et al.
Published: (2023)
Spike-based computation using classical recurrent neural networks
by: De Geeter, Florent, et al.
Published: (2023)
by: De Geeter, Florent, et al.
Published: (2023)
Hypercomplex neural network in time series forecasting of stock data
by: Kycia, Radosław, et al.
Published: (2024)
by: Kycia, Radosław, et al.
Published: (2024)
Flexible inference for animal learning rules using neural networks
by: Liu, Yuhan Helena, et al.
Published: (2025)
by: Liu, Yuhan Helena, et al.
Published: (2025)
Optimal feature rescaling in machine learning based on neural networks
by: Vitrò, Federico Maria, et al.
Published: (2024)
by: Vitrò, Federico Maria, et al.
Published: (2024)
Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
by: Fra, Vittorio
Published: (2025)
by: Fra, Vittorio
Published: (2025)
Fast gradient-free activation maximization for neurons in spiking neural networks
by: Pospelov, Nikita, et al.
Published: (2023)
by: Pospelov, Nikita, et al.
Published: (2023)
A rationale from frequency perspective for grokking in training neural network
by: Zhou, Zhangchen, et al.
Published: (2024)
by: Zhou, Zhangchen, et al.
Published: (2024)
Similar Items
-
Efficient event-driven retrieval in high-capacity kernel Hopfield networks
by: Tamamori, Akira
Published: (2026) -
Kernel Logistic Regression Learning for High-Capacity Hopfield Networks
by: Tamamori, Akira
Published: (2025) -
Quantitative Attractor Analysis of High-Capacity Kernel Hopfield Networks
by: Tamamori, Akira
Published: (2025) -
Kernel Ridge Regression for Efficient Learning of High-Capacity Hopfield Networks
by: Tamamori, Akira
Published: (2025) -
Self-Organization and Spectral Mechanism of Attractor Landscapes in High-Capacity Kernel Hopfield Networks
by: Tamamori, Akira
Published: (2025)