{"total":24,"rows":[{"id":"2402.01092","title":"A Dynamical Model of Neural Scaling Laws","created":"2024-02-02","cited_by_count":111,"area":null},{"id":"2205.09653","title":"Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks","created":"2022-05-19","cited_by_count":105,"area":null},{"id":"2405.00592","title":"Scaling and renormalization in high-dimensional regression","created":"2024-05-01","cited_by_count":50,"area":null},{"id":"2304.03408","title":"Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks","created":"2023-04-06","cited_by_count":43,"area":null},{"id":"2302.00375","title":"Bayes-optimal Learning of Deep Random Networks of Extensive-width","created":"2023-02-01","cited_by_count":41,"area":null},{"id":"2302.00401","title":"Deterministic equivalent and error universality of deep random features learning","created":"2023-02-01","cited_by_count":35,"area":null},{"id":"2211.06506","title":"Spectral Evolution and Invariance in Linear-width Neural Networks","created":"2022-11-11","cited_by_count":30,"area":null},{"id":"2212.12147","title":"The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes","created":"2022-12-23","cited_by_count":25,"area":null},{"id":"2212.14457","title":"Bayesian Interpolation with Deep Linear Networks","created":"2022-12-29","cited_by_count":21,"area":null},{"id":"2305.11041","title":"High-dimensional Asymptotics of Denoising Autoencoders","created":"2023-05-18","cited_by_count":19,"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":"2502.02531","title":"Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer","created":"2025-02-04","cited_by_count":19,"area":null},{"id":"2303.00564","title":"Learning curves for deep structured Gaussian feature models","created":"2023-03-01","cited_by_count":13,"area":null},{"id":"2402.13999","title":"Asymptotics of Learning with Deep Structured (Random) Features","created":"2024-02-21","cited_by_count":13,"area":null},{"id":"2502.07998","title":"Adaptive kernel predictors from feature-learning infinite limits of neural networks","created":"2025-02-11","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":"2409.13904","title":"High-dimensional learning of narrow neural networks","created":"2024-09-20","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":"2405.17538","title":"Bayesian RG Flow in Neural Network Field Theories","created":"2024-05-27","cited_by_count":9,"area":null},{"id":"2406.02269","title":"Graph Neural Networks Do Not Always Oversmooth","created":"2024-06-04","cited_by_count":7,"area":null},{"id":"2305.07715","title":"Field theory for optimal signal propagation in ResNets","created":"2023-05-12","cited_by_count":6,"area":null},{"id":"2505.03577","title":"Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime","created":"2025-05-06","cited_by_count":6,"area":null},{"id":"2304.03385","title":"Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training","created":"2023-04-06","cited_by_count":3,"area":null},{"id":"2412.05418","title":"No Free Lunch From Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions","created":"2024-12-06","cited_by_count":1,"area":null}],"next_cursor":null}