{"total":106,"rows":[{"id":"2203.15332","title":"Balanced Multimodal Learning via On-the-fly Gradient Modulation","created":"2022-03-29","cited_by_count":264,"area":null},{"id":"2101.12176","title":"On the Origin of Implicit Regularization in Stochastic Gradient Descent","created":"2021-01-28","cited_by_count":184,"area":null},{"id":"2106.09524","title":"Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity","created":"2021-06-17","cited_by_count":110,"area":null},{"id":"2106.11642","title":"Repulsive Deep Ensembles are Bayesian","created":"2021-06-22","cited_by_count":104,"area":null},{"id":"2107.04562","title":"The Bayesian Learning Rule","created":"2021-07-09","cited_by_count":99,"area":null},{"id":"2206.07136","title":"Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger","created":"2022-06-14","cited_by_count":88,"area":null},{"id":"2102.12470","title":"On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)","created":"2021-02-24","cited_by_count":73,"area":null},{"id":"2105.13462","title":"On Linear Stability of SGD and Input-Smoothness of Neural Networks","created":"2021-05-27","cited_by_count":63,"area":null},{"id":"2109.14119","title":"Stochastic Training is Not Necessary for Generalization","created":"2021-09-29","cited_by_count":63,"area":null},{"id":"2306.04251","title":"Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks","created":"2023-06-07","cited_by_count":39,"area":null},{"id":"2302.08982","title":"(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability","created":"2023-02-17","cited_by_count":36,"area":null},{"id":"2303.02749","title":"Revisiting the Noise Model of Stochastic Gradient Descent","created":"2023-03-05","cited_by_count":36,"area":null},{"id":"2102.04396","title":"SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality","created":"2021-02-08","cited_by_count":35,"area":null},{"id":"2105.13343","title":"Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error","created":"2021-05-27","cited_by_count":33,"area":null},{"id":"2410.11582","title":"On-the-fly Modulation for Balanced Multimodal Learning","created":"2024-10-15","cited_by_count":32,"area":null},{"id":"2106.13897","title":"Implicit Gradient Alignment in Distributed and Federated Learning","created":"2021-06-25","cited_by_count":29,"area":null},{"id":"2206.09841","title":"Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation","created":"2022-06-20","cited_by_count":29,"area":null},{"id":"2305.16038","title":"Implicit bias of SGD in $L_{2}$-regularized linear DNNs: One-way jumps from high to low rank","created":"2023-05-25","cited_by_count":29,"area":null},{"id":"2604.21691","title":"There Will Be a Scientific Theory of Deep Learning","created":"2026-04-23","cited_by_count":29,"area":null},{"id":"2202.03038","title":"Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry","created":"2022-02-07","cited_by_count":28,"area":null},{"id":"2501.06842","title":"SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training","created":"2025-01-12","cited_by_count":28,"area":null},{"id":"2206.07252","title":"Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions","created":"2022-06-15","cited_by_count":23,"area":null},{"id":"2301.08203","title":"An SDE for Modeling SAM: Theory and Insights","created":"2023-01-19","cited_by_count":22,"area":null},{"id":"2301.02312","title":"Training trajectories, mini-batch losses and the curious role of the learning rate","created":"2023-01-05","cited_by_count":21,"area":null},{"id":"2305.01604","title":"The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold","created":"2023-05-02","cited_by_count":21,"area":null},{"id":"2107.09133","title":"The Limiting Dynamics of SGD: Modified Loss, Phase Space Oscillations, and Anomalous Diffusion","created":"2021-07-19","cited_by_count":19,"area":null},{"id":"2104.08604","title":"Lifelong Learning with Sketched Structural Regularization","created":"2021-04-17","cited_by_count":15,"area":null},{"id":"2302.02766","title":"Generalization Bounds with Data-dependent Fractal Dimensions","created":"2023-02-06","cited_by_count":15,"area":null},{"id":"2510.27486","title":"FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models","created":"2025-10-31","cited_by_count":15,"area":null},{"id":"2110.14163","title":"Does the Data Induce Capacity Control in Deep Learning?","created":"2021-10-27","cited_by_count":14,"area":null},{"id":"2205.08187","title":"Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility","created":"2022-05-17","cited_by_count":13,"area":null},{"id":"2203.10736","title":"The activity-weight duality in feed forward neural networks: The geometric determinants of generalization","created":"2022-03-21","cited_by_count":12,"area":null},{"id":"2309.10688","title":"On the different regimes of Stochastic Gradient Descent","created":"2023-09-19","cited_by_count":12,"area":null},{"id":"2104.04874","title":"SGD Implicitly Regularizes Generalization Error","created":"2021-04-10","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":"2108.03947","title":"On the Hyperparameters in Stochastic Gradient Descent with Momentum","created":"2021-08-09","cited_by_count":9,"area":null},{"id":"2203.12330","title":"Predicting the generalization gap in neural networks using topological data analysis","created":"2022-03-23","cited_by_count":9,"area":null},{"id":"2209.14501","title":"On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks","created":"2022-09-29","cited_by_count":9,"area":null},{"id":"2301.13703","title":"Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning","created":"2023-01-31","cited_by_count":9,"area":null},{"id":"2312.14567","title":"Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise","created":"2023-12-22","cited_by_count":9,"area":null},{"id":"2110.05960","title":"Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations","created":"2021-10-11","cited_by_count":8,"area":null},{"id":"2210.17011","title":"A picture of the space of typical learnable tasks","created":"2022-10-31","cited_by_count":7,"area":null},{"id":"2309.03004","title":"A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness","created":"2023-09-06","cited_by_count":7,"area":null},{"id":"2310.19215","title":"On the accuracy and efficiency of group-wise clipping in differentially private optimization","created":"2023-10-30","cited_by_count":7,"area":null},{"id":"2401.09787","title":"Querying Easily Flip-flopped Samples for Deep Active Learning","created":"2024-01-18","cited_by_count":7,"area":null},{"id":"2201.09209","title":"Weight Expansion: A New Perspective on Dropout and Generalization","created":"2022-01-23","cited_by_count":6,"area":null},{"id":"2112.00987","title":"On Large Batch Training and Sharp Minima: A Fokker-Planck Perspective","created":"2021-12-02","cited_by_count":5,"area":null},{"id":"2212.05611","title":"Accelerating Self-Supervised Learning via Efficient Training Strategies","created":"2022-12-11","cited_by_count":5,"area":null},{"id":"2306.03521","title":"Machine learning in and out of equilibrium","created":"2023-06-06","cited_by_count":5,"area":null},{"id":"2101.06509","title":"Phases of learning dynamics in artificial neural networks: with or without mislabeled data","created":"2021-01-16","cited_by_count":4,"area":null},{"id":"2105.08037","title":"Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation","created":"2021-05-17","cited_by_count":4,"area":null},{"id":"2305.17332","title":"Learning Capacity: A Measure of the Effective Dimensionality of a Model","created":"2023-05-27","cited_by_count":4,"area":null},{"id":"2406.00396","title":"Stochastic Resetting Mitigates Latent Gradient Bias of SGD from Label Noise","created":"2024-06-01","cited_by_count":4,"area":null},{"id":"2505.23489","title":"SGD as Free Energy Minimization: A Thermodynamic View on Neural Network Training","created":"2025-05-29","cited_by_count":4,"area":null},{"id":"2106.16200","title":"Revisiting the Effects of Stochasticity for Hamiltonian Samplers","created":"2021-06-30","cited_by_count":3,"area":null},{"id":"2112.01433","title":"Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent","created":"2021-12-02","cited_by_count":3,"area":null},{"id":"2305.05601","title":"Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists","created":"2023-05-09","cited_by_count":3,"area":null},{"id":"2311.00274","title":"Generalization Bounds for Label Noise Stochastic Gradient Descent","created":"2023-11-01","cited_by_count":3,"area":null},{"id":"2407.02322","title":"Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent","created":"2024-07-02","cited_by_count":3,"area":null},{"id":"2510.24160","title":"Identifiable learning of dissipative dynamics","created":"2025-10-28","cited_by_count":3,"area":null},{"id":"2108.09507","title":"Shift-Curvature, SGD, and Generalization","created":"2021-08-21","cited_by_count":2,"area":null},{"id":"2207.01847","title":"PoF: Post-Training of Feature Extractor for Improving Generalization","created":"2022-07-05","cited_by_count":2,"area":null},{"id":"2302.00766","title":"Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds","created":"2023-02-01","cited_by_count":2,"area":null},{"id":"2306.05300","title":"Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances in Flat Directions","created":"2023-06-08","cited_by_count":2,"area":null},{"id":"2506.18588","title":"Optimization-Induced Dynamics of Lipschitz Continuity in Neural Networks","created":"2025-06-23","cited_by_count":2,"area":null},{"id":"2510.21588","title":"Contribution of task-irrelevant stimuli to drift of neural representations","created":"2025-10-24","cited_by_count":2,"area":null},{"id":"2602.05600","title":"On the Superlinear Relationship between SGD Noise Covariance and Loss Landscape Curvature","created":"2026-02-05","cited_by_count":2,"area":null},{"id":"2606.28486","title":"Spectral phase transitions and trainability in neural network learning dynamics","created":"2026-06-26","cited_by_count":2,"area":null},{"id":"2105.02062","title":"Understanding Short-Range Memory Effects in Deep Neural Networks","created":"2021-05-05","cited_by_count":1,"area":null},{"id":"2105.10347","title":"Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics","created":"2021-05-21","cited_by_count":1,"area":null},{"id":"2110.11402","title":"On the Regularization of Autoencoders","created":"2021-10-21","cited_by_count":1,"area":null},{"id":"2206.02001","title":"A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks","created":"2022-06-04","cited_by_count":1,"area":null},{"id":"2210.11257","title":"A note on diffusion limits for stochastic gradient descent","created":"2022-10-20","cited_by_count":1,"area":null},{"id":"2311.14120","title":"Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation","created":"2023-11-23","cited_by_count":1,"area":null},{"id":"2406.14329","title":"Adaptive Adversarial Cross-Entropy Loss for Sharpness-Aware Minimization","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2505.08915","title":"An Analytical Characterization of Sloppiness in Neural Networks: Insights from Linear Models","created":"2025-05-13","cited_by_count":1,"area":null},{"id":"2601.18420","title":"Gradient Regularized Natural Gradients","created":"2026-01-26","cited_by_count":1,"area":null},{"id":"2603.13872","title":"On Interpolation Formulas Describing Neural Network Generalization","created":"2026-03-14","cited_by_count":1,"area":null},{"id":"2605.22480","title":"Implicit Regularization of Mini-Batch Training in Graph Neural Networks","created":"2026-05-21","cited_by_count":1,"area":null},{"id":"2609.38998","title":"Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks","created":"2026-09-30","cited_by_count":1,"area":null},{"id":"2105.14694","title":"Combining resampling and reweighting for faithful stochastic optimization","created":"2021-05-31","cited_by_count":0,"area":null},{"id":"2108.08891","title":"Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators","created":"2021-08-19","cited_by_count":0,"area":null},{"id":"2112.07110","title":"Non-Asymptotic Analysis of Online Multiplicative Stochastic Gradient Descent","created":"2021-12-14","cited_by_count":0,"area":null},{"id":"2203.13987","title":"On the Viability of Monocular Depth Pre-training for Semantic Segmentation","created":"2022-03-26","cited_by_count":0,"area":null},{"id":"2204.02489","title":"Pareto-optimal clustering with the primal deterministic information bottleneck","created":"2022-04-05","cited_by_count":0,"area":null},{"id":"2206.04359","title":"Learning Non-Vacuous Generalization Bounds from Optimization","created":"2022-06-09","cited_by_count":0,"area":null},{"id":"2304.00320","title":"Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability","created":"2023-04-01","cited_by_count":0,"area":null},{"id":"2305.18864","title":"Stochastic Gradient Langevin Dynamics Based on Quantization with Increasing Resolution","created":"2023-05-30","cited_by_count":0,"area":null},{"id":"2309.00699","title":"Geometric Deep Learning: a Temperature Based Analysis of Graph Neural Networks","created":"2023-09-01","cited_by_count":0,"area":null},{"id":"2309.13069","title":"Machine Learning Technique Based Fake News Detection","created":"2023-09-18","cited_by_count":0,"area":null},{"id":"2401.11562","title":"Enhancing selectivity using Wasserstein distance based reweighing","created":"2024-01-21","cited_by_count":0,"area":null},{"id":"2403.12688","title":"SEVEN: Pruning Transformer Model by Reserving Sentinels","created":"2024-03-19","cited_by_count":0,"area":null},{"id":"2410.05807","title":"Extended convexity and smoothness and their applications in deep learning","created":"2024-10-08","cited_by_count":0,"area":null},{"id":"2505.18166","title":"Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression","created":"2025-05-13","cited_by_count":0,"area":null},{"id":"2508.12834","title":"Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective","created":"2025-08-18","cited_by_count":0,"area":null},{"id":"2509.20334","title":"Feature Dynamics as Implicit Data Augmentation: A Depth-Decomposed View on Deep Neural Network Generalization","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2511.07308","title":"Can Stationary Distributions of Scale-Invariant Neural Networks Be Described by the Thermodynamics of an Ideal Gas?","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2601.05586","title":"Poisson Hyperplane Processes with Rectified Linear Units","created":"2026-01-09","cited_by_count":0,"area":null},{"id":"2604.06366","title":"Stochastic Gradient Descent in the Saddle-to-Saddle Regime of Deep Linear Networks","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2605.04054","title":"Endogenous Regime Switching Driven by Scalar-Irreducible Learning Dynamics","created":"2026-04-10","cited_by_count":0,"area":null}],"next_cursor":"100"}