Image de couverture de livre

Are queries and keys always relevant? A case study on Transformer wave functions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Rende, Riccardo, Viteritti, Luciano Loris
Format:	Preprint
Publié:	2024
Sujets:	Disordered Systems and Neural Networks Computation and Language Computational Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Approaching the Thermodynamic Limit with Neural-Network Quantum States
par: Viteritti, Luciano Loris, et autres
Publié: (2026)

Fine-tuning Neural Network Quantum States
par: Rende, Riccardo, et autres
Publié: (2024)

Transformer Wave Function for two dimensional frustrated magnets: emergence of a Spin-Liquid Phase in the Shastry-Sutherland Model
par: Viteritti, Luciano Loris, et autres
Publié: (2023)

Transformer Neural-Network Quantum States for lattice models of spins and fermions: Application to the Ancilla Layer Model
par: Rende, Riccardo, et autres
Publié: (2026)

A simple linear algebra identity to optimize Large-Scale Neural Network Quantum States
par: Rende, Riccardo, et autres
Publié: (2023)

Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians
par: Rende, Riccardo, et autres
Publié: (2025)

Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model
par: Viteritti, Luciano Loris, et autres
Publié: (2026)

Quantum Spin Glass in the Two-Dimensional Disordered Heisenberg Model via Foundation Neural-Network Quantum States
par: Viteritti, Luciano Loris, et autres
Publié: (2025)

Mapping of attention mechanisms to a generalized Potts model
par: Rende, Riccardo, et autres
Publié: (2023)

Low-resolution descriptions of model neural activity reveal hidden features and underlying system properties
par: Aldrigo, Riccardo, et autres
Publié: (2024)

Quantum-inspired Embeddings Projection and Similarity Metrics for Representation Learning
par: Kankeu, Ivan, et autres
Publié: (2025)

Robustness of the Random Language Model
par: Lalegani, Fatemeh, et autres
Publié: (2023)

The QISG suite: high-performance codes for studying Quantum Ising Spin Glasses
par: Bernaschi, Massimo, et autres
Publié: (2024)

Chess variation entropy and engine relevance for humans
par: Barthelemy, Marc
Publié: (2025)

Full transmission of vectorial waves through 3D multiple-scattering media
par: Lin, Ho-Chun, et autres
Publié: (2024)

Variational Monte Carlo with Large Patched Transformers
par: Sprague, Kyle, et autres
Publié: (2023)

DDNet: A Unified Physics-Informed Deep Learning Framework for Semiconductor Device Modeling
par: Riganti, Roberto, et autres
Publié: (2025)

Neural network distillation of orbital dependent density functional theory
par: Medvidović, Matija, et autres
Publié: (2024)

Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV-Vis Extinction Spectra
par: Klinavičius, Tomas, et autres
Publié: (2024)

Renormalization group of topological scattering networks
par: Zhang, Zhe, et autres
Publié: (2024)

Machine Learning Interatomic Potentials with Keras API
par: Rili, James Paolo
Publié: (2024)

Graph-based Descriptors for Condensed Matter
par: Wang, An, et autres
Publié: (2024)

Solving combinatorial optimization problems through stochastic Landau-Lifshitz-Gilbert dynamical systems
par: Chen, Dairong, et autres
Publié: (2024)

The dimensionality of the Hopfield model
par: Erazo, Cristopher, et autres
Publié: (2026)

Mean-squared Energy Difference for Exploring Potential Energy Landscapes of Supercooled Liquids
par: Zhang, Dianmo, et autres
Publié: (2025)

Construction of Machine-Learning Interatomic Potential Under Heat Flux Regularization and Its Application to Power Spectrum Analysis for Silver Chalcogenides
par: Shimamura, Kohei, et autres
Publié: (2022)

Algorithm to generate hierarchical structure of desiccation crack patterns
par: Tarasevich, Yuri Yu., et autres
Publié: (2025)

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
par: Guzman, Marcelo, et autres
Publié: (2025)

Optimizing p-spin models through hypergraph neural networks and deep reinforcement learning
par: Zeng, Li, et autres
Publié: (2026)

Statistical physics for artificial neural networks
par: Pei, Zongrui
Publié: (2025)

Design Principles for Realizable Discrete Surface Embeddings in Physical Systems
par: Kim, Kyungeun, et autres
Publié: (2025)

Machine learning of (1+1)-dimensional directed percolation based on raw and shuffled configurations
par: Jianmin, Shen, et autres
Publié: (2023)

Convolutional transformer wave functions
par: Chen, Ao, et autres
Publié: (2025)

A numerical study of wave-function and matrix-element statistics in the Anderson model of localization
par: Uski, Ville, et autres
Publié: (1998)

Towards a theory of how the structure of language is acquired by deep neural networks
par: Cagnetta, Francesco, et autres
Publié: (2024)

Synaptic Theory of Chunking in Working Memory
par: Zhong, Weishun, et autres
Publié: (2024)

Fast Analysis of the OpenAI O1-Preview Model in Solving Random K-SAT Problem: Does the LLM Solve the Problem Itself or Call an External SAT Solver?
par: Marino, Raffaele
Publié: (2024)

Impact of ChatGPT on the writing style of condensed matter physicists
par: Xu, Shaojun, et autres
Publié: (2024)

On the Emergence of Linear Analogies in Word Embeddings
par: Korchinski, Daniel J., et autres
Publié: (2025)

Deep networks learn to parse uniform-depth context-free languages from local statistics
par: Parley, Jack T., et autres
Publié: (2026)