{"total":62,"rows":[{"id":"2110.06296","title":"The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks","created":"2021-10-12","cited_by_count":291,"area":null},{"id":"2205.10487","title":"Scaling Laws and Interpretability of Learning from Repeated Data","created":"2022-05-21","cited_by_count":152,"area":null},{"id":"2105.12221","title":"Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances","created":"2021-05-25","cited_by_count":127,"area":null},{"id":"2211.08403","title":"REPAIR: REnormalizing Permuted Activations for Interpolation Repair","created":"2022-11-15","cited_by_count":120,"area":null},{"id":"2210.16859","title":"A Solvable Model of Neural Scaling Laws","created":"2022-10-30","cited_by_count":116,"area":null},{"id":"2206.10012","title":"Limitations of the NTK for Understanding Generalization in Deep Learning","created":"2022-06-20","cited_by_count":35,"area":null},{"id":"2107.11228","title":"Taxonomizing local versus global structure in neural network loss landscapes","created":"2021-07-23","cited_by_count":34,"area":null},{"id":"2103.01519","title":"Hessian Eigenspectra of More Realistic Nonlinear Models","created":"2021-03-02","cited_by_count":33,"area":null},{"id":"2112.03215","title":"Multi-scale Feature Learning Dynamics: Insights for Double Descent","created":"2021-12-06","cited_by_count":24,"area":null},{"id":"2204.06274","title":"Overparameterized Linear Regression under Adversarial Attacks","created":"2022-04-13","cited_by_count":21,"area":null},{"id":"2110.11749","title":"Feature Learning and Signal Propagation in Deep Neural Networks","created":"2021-10-22","cited_by_count":18,"area":null},{"id":"2101.08732","title":"Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning","created":"2021-01-21","cited_by_count":17,"area":null},{"id":"2210.12760","title":"On double-descent in uncertainty quantification in overparametrized models","created":"2022-10-23","cited_by_count":16,"area":null},{"id":"2103.05524","title":"On the interplay between data structure and loss function in classification problems","created":"2021-03-09","cited_by_count":13,"area":null},{"id":"2405.00228","title":"Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion","created":"2024-04-30","cited_by_count":12,"area":null},{"id":"2301.12309","title":"On the Lipschitz Constant of Deep Networks and Double Descent","created":"2023-01-28","cited_by_count":11,"area":null},{"id":"2107.09437","title":"Edge of chaos as a guiding principle for modern neural network training","created":"2021-07-20","cited_by_count":10,"area":null},{"id":"2205.12937","title":"Mitigating multiple descents: A model-agnostic framework for risk monotonization","created":"2022-05-25","cited_by_count":10,"area":null},{"id":"2506.03470","title":"Models of Heavy-Tailed Mechanistic Universality","created":"2025-06-04","cited_by_count":10,"area":null},{"id":"2302.05059","title":"Effects of noise on the overparametrization of quantum neural networks","created":"2023-02-10","cited_by_count":9,"area":null},{"id":"2206.01399","title":"Generalization for multiclass classification with overparameterized linear models","created":"2022-06-03","cited_by_count":8,"area":null},{"id":"2209.10080","title":"Deep Double Descent via Smooth Interpolation","created":"2022-09-21","cited_by_count":8,"area":null},{"id":"2402.01089","title":"No Free Prune: Information-Theoretic Barriers to Pruning at Initialization","created":"2024-02-02","cited_by_count":8,"area":null},{"id":"2102.03793","title":"Tilting the playing field: Dynamical loss functions for machine learning","created":"2021-02-07","cited_by_count":7,"area":null},{"id":"2102.06849","title":"Distilling Double Descent","created":"2021-02-13","cited_by_count":7,"area":"distill"},{"id":"2407.05664","title":"How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning","created":"2024-07-08","cited_by_count":6,"area":null},{"id":"2301.13516","title":"Recurrences reveal shared causal drivers of complex time series","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2107.00797","title":"Mitigating deep double descent by concatenating inputs","created":"2021-07-02","cited_by_count":3,"area":null},{"id":"2404.13404","title":"Solution space and storage capacity of fully connected two-layer neural networks with generic activation functions","created":"2024-04-20","cited_by_count":3,"area":null},{"id":"2505.02604","title":"Connecting Independently Trained Modes via Layer-Wise Connectivity","created":"2025-05-05","cited_by_count":3,"area":null},{"id":"2112.03660","title":"A generalization gap estimation for overparameterized models via the Langevin functional variance","created":"2021-12-07","cited_by_count":2,"area":null},{"id":"2302.13259","title":"Can we avoid Double Descent in Deep Neural Networks?","created":"2023-02-26","cited_by_count":2,"area":null},{"id":"2510.06401","title":"The Effect of Label Noise on the Information Content of Neural Representations","created":"2025-10-07","cited_by_count":2,"area":null},{"id":"2512.11866","title":"Phase transitions reveal hierarchical structure in deep neural networks","created":"2025-12-05","cited_by_count":2,"area":null},{"id":"2606.20299","title":"Statistical Properties of Training & Generalization","created":"2026-06-18","cited_by_count":2,"area":null},{"id":"2101.07256","title":"Fitting very flexible models: Linear regression with large numbers of parameters","created":"2021-01-15","cited_by_count":1,"area":null},{"id":"2106.02100","title":"Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels","created":"2021-06-03","cited_by_count":1,"area":null},{"id":"2109.13215","title":"Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective","created":"2021-09-27","cited_by_count":1,"area":null},{"id":"2208.13080","title":"Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models","created":"2022-08-27","cited_by_count":1,"area":null},{"id":"2302.09526","title":"Mixed Semi-Supervised Generalized-Linear-Regression with Applications to Deep-Learning and Interpolators","created":"2023-02-19","cited_by_count":1,"area":null},{"id":"2303.07925","title":"Deep incremental learning models for financial temporal tabular datasets with distribution shifts","created":"2023-03-14","cited_by_count":1,"area":null},{"id":"2504.11830","title":"Emergence of Computational Structure in a Neural Network Physics Simulator","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2602.05849","title":"Visualizing the loss landscapes of physics-informed neural networks","created":"2026-02-05","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":"2102.01194","title":"A Statistician Teaches Deep Learning","created":"2021-01-29","cited_by_count":0,"area":null},{"id":"2201.07934","title":"Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks","created":"2022-01-03","cited_by_count":0,"area":null},{"id":"2210.03762","title":"Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning","created":"2022-10-07","cited_by_count":0,"area":null},{"id":"2305.13536","title":"Subspace-Configurable Networks","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2306.05583","title":"Gibbs-Based Information Criteria and the Over-Parameterized Regime","created":"2023-06-08","cited_by_count":0,"area":null},{"id":"2306.13474","title":"Efficient Online Processing with Deep Neural Networks","created":"2023-06-23","cited_by_count":0,"area":null},{"id":"2306.15015","title":"Scaling and Resizing Symmetry in Feedforward Networks","created":"2023-06-26","cited_by_count":0,"area":null},{"id":"2308.16596","title":"The Quest of Finding the Antidote to Sparse Double Descent","created":"2023-08-31","cited_by_count":0,"area":null},{"id":"2401.12610","title":"The twin peaks of learning neural networks","created":"2024-01-23","cited_by_count":0,"area":null},{"id":"2410.00699","title":"Investigating the Impact of Model Complexity in Large Language Models","created":"2024-10-01","cited_by_count":0,"area":null},{"id":"2410.10690","title":"Dynamical loss functions shape landscape topography and improve learning in artificial neural networks","created":"2024-10-14","cited_by_count":0,"area":null},{"id":"2501.05550","title":"Emergent weight morphologies in deep neural networks","created":"2025-01-09","cited_by_count":0,"area":null},{"id":"2504.01737","title":"Enlightenment Period Improving DNN Performance","created":"2025-04-02","cited_by_count":0,"area":null},{"id":"2511.22004","title":"On the Effect of Regularization on Nonparametric Mean-Variance Regression","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2607.07845","title":"Explaining Near-Zero Hessian Eigenvalues Through Approximate Symmetries in Neural Networks","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.27482","title":"Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.09396","title":"From Objectives to What Models Learn: A Landau Theory of Invariant Learning","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2609.02373","title":"Percolation Dynamics in Optimization: Variance Cascades and Nested Symmetry","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}