{"total":31,"rows":[{"id":"2410.01131","title":"nGPT: Normalized Transformer with Representation Learning on the Hypersphere","created":"2024-10-01","cited_by_count":70,"area":null},{"id":"2406.03136","title":"Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models","created":"2024-06-05","cited_by_count":31,"area":null},{"id":"2411.07602","title":"Circuit Complexity Bounds for RoPE-based Transformer Architecture","created":"2024-11-12","cited_by_count":26,"area":null},{"id":"2408.13233","title":"Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time","created":"2024-08-23","cited_by_count":22,"area":null},{"id":"2410.11268","title":"Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent","created":"2024-10-15","cited_by_count":22,"area":null},{"id":"2405.17822","title":"Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action","created":"2024-05-28","cited_by_count":19,"area":null},{"id":"2408.12151","title":"A Tighter Complexity Analysis of SparseGPT","created":"2024-08-22","cited_by_count":19,"area":null},{"id":"2410.10165","title":"HSR-Enhanced Sparse Attention Acceleration","created":"2024-10-14","cited_by_count":19,"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":"2404.03900","title":"Nonparametric Modern Hopfield Models","created":"2024-04-05","cited_by_count":16,"area":null},{"id":"2406.18400","title":"Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers","created":"2024-06-26","cited_by_count":15,"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":"2412.18040","title":"Theoretical Constraints on the Expressive Power of $\\mathsf{RoPE}$-based Tensor Attention Transformers","created":"2024-12-23","cited_by_count":12,"area":null},{"id":"2506.10801","title":"Dense Associative Memory with Epanechnikov Energy","created":"2025-06-12","cited_by_count":11,"area":null},{"id":"2412.05562","title":"Modern Hopfield Networks Require Chain-of-Thought to Solve $\\mathsf{NC}^1$-Hard Problems","created":"2024-12-07","cited_by_count":9,"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":"2505.00598","title":"Fast and Low-Cost Genomic Foundation Models via Outlier Removal","created":"2025-05-01","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":"2411.01663","title":"Unlocking the Theory Behind Scaling 1-Bit Neural Networks","created":"2024-11-03","cited_by_count":4,"area":null},{"id":"2409.01688","title":"Differentially Private Kernel Density Estimation","created":"2024-09-03","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":"2509.23162","title":"Dense associative memory for Gaussian distributions","created":"2025-09-27","cited_by_count":2,"area":null},{"id":"2405.06003","title":"Binary Hypothesis Testing for Softmax Models and Leverage Score Models","created":"2024-05-09","cited_by_count":1,"area":null},{"id":"2511.20609","title":"Adaptive Hopfield Network: Rethinking Similarities in Associative Memory","created":"2025-11-25","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":"2409.16408","title":"Modern Hopfield Networks meet Encoded Neural Representations -- Addressing Practical Considerations","created":"2024-09-24","cited_by_count":0,"area":null},{"id":"2508.01395","title":"Effects of Feature Correlations on Associative Memory Capacity","created":"2025-08-02","cited_by_count":0,"area":null},{"id":"2606.10238","title":"Hyperbolic Neural Population Geometry Benefits Computation","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.11391","title":"Recursive Binding on a Budget: Subspace Carving in Order-p Tensor Memories","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2609.01849","title":"SSAKG 2.0: An Open-Source Package for Structural Associative Sequence Memory and Context-Based Retrieval","created":"2026-09-01","cited_by_count":0,"area":null}],"next_cursor":null}