{"total":23,"rows":[{"id":"2305.12827","title":"Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models","created":"2023-05-22","cited_by_count":221,"area":null},{"id":"2305.18270","title":"How Two-Layer Neural Networks Learn, One (Giant) Step at a Time","created":"2023-05-29","cited_by_count":68,"area":null},{"id":"2209.14863","title":"Neural Networks Efficiently Learn Low-Dimensional Representations with SGD","created":"2022-09-29","cited_by_count":63,"area":null},{"id":"2405.15459","title":"Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions","created":"2024-05-24","cited_by_count":37,"area":null},{"id":"2307.11807","title":"Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks","created":"2023-07-21","cited_by_count":19,"area":null},{"id":"2503.02877","title":"Weak-to-Strong Generalization Even in Random Feature Networks, Provably","created":"2025-03-04","cited_by_count":15,"area":null},{"id":"2512.20607","title":"Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures","created":"2025-12-23","cited_by_count":15,"area":null},{"id":"2410.04642","title":"The Optimization Landscape of SGD Across the Feature Learning Strength","created":"2024-10-06","cited_by_count":13,"area":null},{"id":"2410.08194","title":"Features are fate: a theory of transfer learning in high-dimensional regression","created":"2024-10-10","cited_by_count":13,"area":null},{"id":"2406.03260","title":"Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers","created":"2024-06-05","cited_by_count":12,"area":null},{"id":"2502.03210","title":"From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning","created":"2025-02-05","cited_by_count":10,"area":null},{"id":"2506.16884","title":"The Importance of Being Lazy: Scaling Limits of Continual Learning","created":"2025-06-20","cited_by_count":6,"area":null},{"id":"2406.12615","title":"When Are Bias-Free ReLU Networks Effectively Linear Networks?","created":"2024-06-18","cited_by_count":4,"area":null},{"id":"2311.09690","title":"CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs","created":"2023-11-16","cited_by_count":3,"area":null},{"id":"2502.10162","title":"Revisiting Generalization Power of a DNN in Terms of Symbolic Interactions","created":"2025-02-14","cited_by_count":2,"area":null},{"id":"2502.16331","title":"A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis","created":"2025-02-22","cited_by_count":2,"area":null},{"id":"2305.13141","title":"Tight conditions for when the NTK approximation is valid","created":"2023-05-22","cited_by_count":1,"area":null},{"id":"2507.19680","title":"Feature learning is decoupled from generalization in high capacity neural networks","created":"2025-07-25","cited_by_count":1,"area":null},{"id":"2510.26653","title":"Towards Reliable Sea Ice Drift Estimation in the Arctic Deep Learning Optical Flow on RADARSAT-2","created":"2025-10-30","cited_by_count":1,"area":null},{"id":"2605.23591","title":"Asymmetric Scaling Laws from Sparse Features","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2510.15174","title":"A simple mean field model of feature learning","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2602.00827","title":"Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2603.07249","title":"LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction","created":"2026-03-07","cited_by_count":0,"area":null}],"next_cursor":null}