Cover Image

Hyperparameter Transfer for Dense Associative Memories

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Holtzman, Roi, Krotov, Dmitry, Hanin, Boris
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dense Associative Memory with Epanechnikov Energy
by: Hoover, Benjamin, et al.
Published: (2025)

Hyperparameter Transfer with Mixture-of-Expert Layers
by: Jiang, Tianze, et al.
Published: (2026)

Dense Associative Memory Through the Lens of Random Features
by: Hoover, Benjamin, et al.
Published: (2024)

Principled Architecture-aware Scaling of Hyperparameters
by: Chen, Wuyang, et al.
Published: (2024)

Modern Methods in Associative Memory
by: Krotov, Dmitry, et al.
Published: (2025)

Improved Robustness and Hyperparameter Selection in the Dense Associative Memory
by: McAlister, Hayden, et al.
Published: (2024)

Deep Clustering with Associative Memories
by: Saha, Bishwajit, et al.
Published: (2026)

Learning Rate Transfer in Normalized Transformers
by: Shigida, Boris, et al.
Published: (2026)

Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data
by: Pham, Bao, et al.
Published: (2026)

Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models
by: Hoover, Benjamin, et al.
Published: (2023)

Bayesian Inference with Shaped Deep Non-linear MLPs
by: Hanin, Boris, et al.
Published: (2026)

Implicit Bias of the JKO Scheme
by: Halmos, Peter, et al.
Published: (2025)

Dense Associative Memories with Analog Circuits
by: Bacvanski, Marc Gong, et al.
Published: (2025)

Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
by: Pham, Bao, et al.
Published: (2025)

Bayesian Inference with Deep Weakly Nonlinear Networks
by: Hanin, Boris, et al.
Published: (2024)

A Biologically Plausible Dense Associative Memory with Exponential Capacity
by: Kafraj, Mohadeseh Shafiei, et al.
Published: (2026)

Deep Neural Nets as Hamiltonians
by: Winer, Mike, et al.
Published: (2025)

Understanding the Mechanisms of Fast Hyperparameter Transfer
by: Ghosh, Nikhil, et al.
Published: (2025)

Saddle Hierarchy in Dense Associative Memory
by: Thériault, Robin, et al.
Published: (2025)

On Optimal Hyperparameters for Differentially Private Deep Transfer Learning
by: Rehn, Aki, et al.
Published: (2025)

Hyperparameter Transfer Laws for Non-Recurrent Multi-Path Neural Networks
by: Wu, Shenxi, et al.
Published: (2026)

Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
by: Carstensen, Timur, et al.
Published: (2025)

Hyperparameter Transfer Enables Consistent Gains of Matrix-Preconditioned Optimizers Across Scales
by: Qiu, Shikai, et al.
Published: (2025)

Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators
by: Li, Shanda, et al.
Published: (2025)

NRGPT: An Energy-based Alternative for GPT
by: Dehmamy, Nima, et al.
Published: (2025)

Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance
by: Paul, Roi
Published: (2026)

Information Theoretic Lower Bounds for Information Theoretic Upper Bounds
by: Livni, Roi
Published: (2023)

Quantitative CLTs in Deep Neural Networks
by: Favaro, Stefano, et al.
Published: (2023)

Completed Hyperparameter Transfer across Modules, Width, Depth, Batch and Duration
by: Mlodozeniec, Bruno, et al.
Published: (2025)

Overtuning in Hyperparameter Optimization
by: Schneider, Lennart, et al.
Published: (2025)

Neuron-Astrocyte Associative Memory
by: Kozachkov, Leo, et al.
Published: (2023)

Geometric Entropy and Retrieval Phase Transitions in Continuous Thermal Dense Associative Memory
by: Petrova, Tatiana, et al.
Published: (2026)

Hyperparameter Selection in Continual Learning
by: Lee, Thomas L., et al.
Published: (2024)

Hyperparameter Optimization in Machine Learning
by: Franceschi, Luca, et al.
Published: (2024)

The Role of Hyperparameters in Predictive Multiplicity
by: Cavus, Mustafa, et al.
Published: (2025)

Losing dimensions: Geometric memorization in generative diffusion
by: Achilli, Beatrice, et al.
Published: (2024)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate
by: Kalra, Dayal Singh, et al.
Published: (2026)

Frequency-Based Hyperparameter Selection in Games
by: Sanyal, Aniket, et al.
Published: (2026)

Stochastic Thermodynamics of Associative Memory
by: Rooke, Spencer, et al.
Published: (2026)