{"total":71,"rows":[{"id":"2307.05782","title":"Large Language Models","created":"2023-07-11","cited_by_count":780,"area":null},{"id":"2309.05858","title":"Uncovering mesa-optimization algorithms in Transformers","created":"2023-09-11","cited_by_count":100,"area":null},{"id":"2306.01129","title":"White-Box Transformers via Sparse Rate Reduction","created":"2023-06-01","cited_by_count":80,"area":null},{"id":"2309.12673","title":"On Sparse Modern Hopfield Model","created":"2023-09-22","cited_by_count":42,"area":null},{"id":"2404.03828","title":"Outlier-Efficient Hopfield Layers for Large Transformer-Based Models","created":"2024-04-04","cited_by_count":38,"area":null},{"id":"2410.13821","title":"Artificial Kuramoto Oscillatory Neurons","created":"2024-10-17","cited_by_count":38,"area":null},{"id":"2312.17346","title":"STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction","created":"2023-12-28","cited_by_count":35,"area":null},{"id":"2507.02092","title":"Energy-Based Transformers are Scalable Learners and Thinkers","created":"2025-07-02","cited_by_count":31,"area":"lr"},{"id":"2306.09869","title":"Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models","created":"2023-06-16","cited_by_count":30,"area":"diffusion"},{"id":"2404.03830","title":"BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model","created":"2024-04-04","cited_by_count":21,"area":null},{"id":"2410.23126","title":"Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes","created":"2024-10-30","cited_by_count":19,"area":null},{"id":"2404.07389","title":"Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models","created":"2024-04-10","cited_by_count":18,"area":"diffusion"},{"id":"2405.06394","title":"Memory Mosaics","created":"2024-05-10","cited_by_count":16,"area":null},{"id":"2408.04093","title":"Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters","created":"2024-08-07","cited_by_count":13,"area":null},{"id":"2405.08707","title":"Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory","created":"2024-05-14","cited_by_count":12,"area":null},{"id":"2501.08339","title":"Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach","created":"2025-01-02","cited_by_count":12,"area":null},{"id":"2410.24153","title":"Dense Associative Memory Through the Lens of Random Features","created":"2024-10-31","cited_by_count":8,"area":null},{"id":"2512.16762","title":"NRGPT: An Energy-based Alternative for GPT","created":"2025-12-18","cited_by_count":8,"area":null},{"id":"2502.05164","title":"In-context denoising with one-layer transformers: connections between attention and associative memory retrieval","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2406.12220","title":"Hierarchical Associative Memory, Parallelized MLP-Mixer, and Symmetry Breaking","created":"2024-06-18","cited_by_count":5,"area":null},{"id":"2406.13661","title":"Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive review","created":"2024-06-19","cited_by_count":5,"area":null},{"id":"2505.19458","title":"Recurrent Self-Attention Dynamics: An Energy-Agnostic Perspective from Jacobians","created":"2025-05-26","cited_by_count":5,"area":null},{"id":"2602.11534","title":"Krause Synchronization Transformers","created":"2026-02-12","cited_by_count":5,"area":null},{"id":"2603.07276","title":"Variational Flow Maps: Make Some Noise for One-Step Conditional Generation","created":"2026-03-07","cited_by_count":5,"area":null},{"id":"2309.12862","title":"Associative Transformer","created":"2023-09-22","cited_by_count":4,"area":null},{"id":"2401.04191","title":"Dense Hopfield Networks in the Teacher-Student Setting","created":"2024-01-08","cited_by_count":4,"area":"distill"},{"id":"2410.07441","title":"Zero-Shot Generalization of Vision-Based RL Without Data Augmentation","created":"2024-10-09","cited_by_count":4,"area":"deeprl"},{"id":"2411.17182","title":"An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models","created":"2024-11-26","cited_by_count":4,"area":null},{"id":"2304.13061","title":"iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-Mixer","created":"2023-04-25","cited_by_count":3,"area":null},{"id":"2505.11719","title":"Zero-Shot Visual Generalization in Robot Manipulation","created":"2025-05-16","cited_by_count":3,"area":"offlinerl"},{"id":"2505.15863","title":"Generative AI for Autonomous Driving: A Review","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2511.00907","title":"Transformers as Intrinsic Optimizers: Forward Inference through the Energy Principle","created":"2025-11-02","cited_by_count":3,"area":null},{"id":"2502.11646","title":"Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2507.05147","title":"Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings","created":"2025-07-07","cited_by_count":2,"area":null},{"id":"2509.23162","title":"Dense associative memory for Gaussian distributions","created":"2025-09-27","cited_by_count":2,"area":null},{"id":"2601.00963","title":"Deep Clustering with Associative Memories","created":"2026-01-02","cited_by_count":2,"area":null},{"id":"2602.07697","title":"On the Infinite Width and Depth Limits of Predictive Coding Networks","created":"2026-02-07","cited_by_count":2,"area":null},{"id":"2603.06875","title":"Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy","created":"2026-03-06","cited_by_count":2,"area":null},{"id":"2603.14717","title":"Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention","created":"2026-03-16","cited_by_count":2,"area":null},{"id":"2607.10128","title":"Energy-guided Recursive Model","created":"2026-07-11","cited_by_count":2,"area":"lr"},{"id":"2311.18434","title":"Exploring the Temperature-Dependent Phase Transition in Modern Hopfield Networks","created":"2023-11-30","cited_by_count":1,"area":null},{"id":"2406.08862","title":"Cognitively Inspired Energy-Based World Models","created":"2024-06-13","cited_by_count":1,"area":"wm"},{"id":"2410.13866","title":"Associative memory and dead neurons","created":"2024-10-02","cited_by_count":1,"area":null},{"id":"2503.19922","title":"Neural Learning Rules from Associative Networks Theory","created":"2025-03-11","cited_by_count":1,"area":null},{"id":"2505.11635","title":"The Gaussian-Multinoulli Restricted Boltzmann Machine: A Potts Model Extension of the GRBM","created":"2025-05-16","cited_by_count":1,"area":"lr"},{"id":"2506.11043","title":"A Framework for Non-Linear Attention via Modern Hopfield Networks","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2508.12787","title":"Wavy Transformer","created":"2025-08-18","cited_by_count":1,"area":null},{"id":"2511.20698","title":"On the Role of Hidden States of Modern Hopfield Network in Transformer","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2601.14096","title":"Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2604.00277","title":"Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2604.05042","title":"Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization","created":"2026-04-06","cited_by_count":1,"area":null},{"id":"2606.24543","title":"Reasoning as Attractor Dynamics: Latent Memory Retrieval via Gibbs-Weighted Energy Minimization","created":"2026-06-23","cited_by_count":1,"area":"lr"},{"id":"2409.16482","title":"Generative AI-driven forecasting of oil production","created":"2024-09-24","cited_by_count":0,"area":null},{"id":"2503.02057","title":"Hebbian learning the local structure of language","created":"2025-03-03","cited_by_count":0,"area":null},{"id":"2505.03648","title":"Binding threshold units with artificial oscillatory neurons","created":"2025-05-06","cited_by_count":0,"area":null},{"id":"2507.10143","title":"Deep Recurrence for Dynamical Segmentation Models","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2512.22324","title":"DeMoGen: Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models","created":"2025-12-26","cited_by_count":0,"area":"diffusion"},{"id":"2603.03464","title":"Graph Hopfield Networks: Energy-Based Node Classification with Associative Memory","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2603.20656","title":"Sinkhorn Based Associative Memory Retrieval Using Spherical Hellinger Kantorovich Dynamics","created":"2026-03-21","cited_by_count":0,"area":null},{"id":"2604.12811","title":"Algorithmic Analysis of Dense Associative Memory: Finite-Size Guarantees and Adversarial Robustness","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2604.26841","title":"Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data","created":"2026-04-29","cited_by_count":0,"area":"diffusion"},{"id":"2605.07588","title":"Revisiting Transformer Layer Parameterization Through Causal Energy Minimization","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.12478","title":"Boltzmann Attention: Learnable Ising Couplings for Cooperative Attention","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2606.31110","title":"Explaining Machine Learning and Memorization with Statistical Mechanics","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.19195","title":"Free energy landscape of Dense Associative Memory","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2607.23634","title":"Variational-Ising-Attention:Tailored Attention Matters for Science","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2608.19331","title":"Deep neural networks as lattice gauge theories","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.10976","title":"Phases in a class of associative memories via hidden neurons","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.32350","title":"Analog-Friendly Predictive Coding without Activation Derivatives","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.36942","title":"Safe-by-Design Learning via Energy-based Neural Networks","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.19552","title":"Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}