Adam

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Period ending 2026-09-14

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Period ending 2026-09-07

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  1. Beyond Quadratic Loss: The Stability Phase Diagram of Adam

    Sep 16, 2026Gaoxiang Tang, Huanran Chen, Ziming LiuAdamConvex Loss

  2. Dimension-Corrected Hitting Times for Heavy-Tailed Spectral Emergence in Neural Optimizer Dynamics

    Sep 14, 2026Zongmin LiuHeavy-Tailed NoiseAdam

  3. A Full Adam Theorem for Spectral Heavy-Tail Onset

    Sep 14, 2026Zongmin LiuAdam

  4. AdamX: Cosine similarity meets gradient descent

    Sep 10, 2026Francisco Caldas, Ruben Belo, Cláudia SoaresAdamZeroth-Order Optimization

  5. Muon-C: Operator-Aligned Muon for Convolutional Kernels

    Sep 9, 2026Jiaxin Qing, Lexin LiMuonDeep Unfolding Networks

  6. SkillAdam: Stable and Efficient Skill Evolution for Agents

    Sep 8, 2026Gaoyuan Li, Meihao Fan, Yizhe Liu +7Skill EvolutionAgent Skill Libraries

  7. Equivariance Breaks the Learning Rate

    Sep 8, 2026Andrei Manolache, Mathias NiepertAdamEquivariance

  8. Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings

    Sep 2, 2026Frank Hu, Shriram Chennakesavalu, David GraffAdaptive OptimizersFrontiers

  9. Stochastic Optimization of Tree Tensor Networks

    Sep 1, 2026Marius Willner, Maximilian Scharf, André Uschmajew +2Tensor NetworksNeural Network Optimization

  10. Convergence rates for the RMSprop optimizer with full control of the hyperparameters

    Aug 31, 2026Steffen Dereich, Arnulf JentzenAdamGradient Descent

  11. Beyond Dense Adam States: Adaptive Log-Space Quantization for Memory-Efficient Optimizers

    Aug 23, 2026Yan WangLow-Bit QuantizationAdam

  12. Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling

    Aug 16, 2026Junbo Jacob Lian, Huiling Chen, Hanzhang Qin +1AdamCandidate

  13. Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization

    Aug 13, 2026Zhixin Ren, Yau Lyu, Congrong Li +2AdamNeural Network Optimization

  14. Information Bottleneck under Perfect Privacy

    Aug 11, 2026Junle Zhong, Mohamad Assaad, Sreejith SreekumarInformation BottleneckKullback-Leibler Regularization

  15. Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network

    Aug 11, 2026Karl Pierce, Yuehaw Khoo, Haizhao YangLow-Rank Tensor DecompositionHigh-Dimensional

  16. MoNo: Multiscale Optimal Transport Neural Operator for Solving PDEs on General Geometries

    Aug 10, 2026Zijiang Yang, Xiaomeng Wu, Dongmei FuNeural OperatorsPartial Differential Equations

  17. Input convex neural networks as surrogates in mathematical optimisation

    Aug 10, 2026Yu Liu, Jan Kronqvist, Fabricio OliveiraMixed-Integer Linear ProgrammingSurrogate Gradient

  18. Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods

    Aug 9, 2026Sarthak Mahapatra, Zihan Zhou, Khatoon Khedri +2Graphics Processing Unit UtilizationLow-Precision Training

  19. A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization

    Aug 8, 2026Víctor GallegoEvolutionary SearchHistorical Optimization Experiences

  20. The Loss Does Not See the Basis, but Adam Does

    Aug 5, 2026Devender SinghLow-Rank FactorizationAdam

  21. MESH: Memory-Efficient Sinkhorn Optimization for Mixture-of-Experts Training

    Aug 5, 2026Masato FujitakeAdamSinkhorn

  22. Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language

    Aug 5, 2026Shengxin Kong, Liwen Xu, Jingwen FuNeural Partial Differential Equation SolversEvolutionary Search

  23. Muon Meets Mamba: Spectral Optimization for State Space Models

    Aug 4, 2026Arslan Battalov, Karim Kramin, Alexander Markotenko +1MuonState Space Models

  24. Exploiting Separability in Multi-Scale Grey-Box Bayesian Optimization

    Aug 4, 2026Joshua E. Hammond, Tyler A. Soderstrom, Brian A. Korgel +1Black-Box OptimizationBayesian Optimization

  25. Joint Affine Spectral Shaping: Coupling Weight and Bias Updates Beyond Weight-Only Muon

    Aug 4, 2026Gongyue Zhang, Honghai LiuAffineMuon

  26. AOS: Adaptive Optimizer Switching via Training-State Signals for Faster Convergence and Better Generalization

    Aug 3, 2026Alok Kumar Pandey, Umang Chaturvedi, Aatish Rana +1Adaptive OptimizersNeural Network Optimization

  27. The Convergence Behavior of Adam under Heavy-Tailed Noise

    Jul 29, 2026Yijiang PangAdamHeavy-Tailed Noise

  28. Reconstructing Backpropagation from Forward Fluctuations in Noise-modulated Neural Networks

    Jul 29, 2026Shuhei IkemotoBackpropagationNeuromorphic Hardware

  29. Guiding Posterior Exploration with Optimizer-Derived Geometry

    Jul 28, 2026Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff +1Adaptive SamplingBayesian Neural Networks

  30. PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM

    Jul 27, 2026Kart-Leong LimVariational InferenceAdam

  31. Variational Boosting for Physics-Informed Neural Networks

    Jul 27, 2026Pavlos Protopapas, Kaylee VoConventional Physics-Informed Neural NetworksNeural Network Optimization

  32. Operator Neural Jump ODEs: L^2-optimal prediction in function spaces

    Jul 25, 2026Florian Krach, Oliver Löthgren, Josef TeichmannNeural Ordinary Differential EquationsNeural-Network Approximation

  33. Hidden Boundary Motion in Transformer Optimization: Function-Space Orthogonalization of Affine Weight and Bias Updates

    Jul 24, 2026Zhang Gongyue, Sheng Yixuan, Liu donghan +3Transformer VariantAdam

  34. Hyperball May Not Be a Free Lunch

    Jul 24, 2026Yihao Xiao, Jialong Sun, Zitian Gao +5Learning RatesAdaptive Optimizers

  35. Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectives

    Jul 24, 2026Marcantonio Bracale Syrnicov, Federico Pierucci, Matteo Prandi +4Renewable PenetrationIncentives

  36. Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers

    Jul 24, 2026Zhangyong Liang, Huanhuan GaoNeural Partial Differential Equation SolversRiemannian Optimization

  37. Searching the Space of Feed-Forward Neural-Network Weight-Update Rules with Fixed Depth Symbolic Regression

    Jul 23, 2026Charles Brum, Edward FinkelsteinSymbolic RegressionNeural Network Optimization

  38. A Defense of the Quadratic Model

    Jul 23, 2026Alexandru Meterez, Pranav Ajit Nair, Depen Morwani +3Hessian EigenvalueNeural Network Optimization

  39. Where Should Optimizer State Live? Tiered State Allocation for Memory-Efficient Mixture-of-Experts Training

    Jul 21, 2026Nuemaan MalikAdamPeak Memory

  40. ADMM-Based Safety-Critical Distributed NMPC for Cooperative Transportation by Quadrupedal Robots

    Jul 18, 2026Ruturaj S. Sambhus, Kapi Ketan Mehta, Yicheng Zeng +1Multi-Robot SystemsModel Predictive Control

  41. Backpropagation-Free Trunk Training via the Split Forward Gradients

    Jul 18, 2026Tian Qin, Wei-Min HuangBackpropagationGradcam

  42. When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets

    Jul 17, 2026Chao Jiang, Yulong Ye, Tao Chen +1Optimizer DesignBudgets

  43. When Does Muon Help Agentic Reinforcement Learning?

    Jul 17, 2026Kai Ruan, Jinghao Lin, Zihe Huang +4MuonAdam

  44. Adaptive Runge-Kutta Step Control Buys Training Loss, Not Generalization: An Honest Compute-Matched Study of RK-Adam Optimizers

    Jul 16, 2026Akhilesh GogikarAdamLearning Rates

  45. LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

    Jul 15, 2026Nilay Anurag, Shital Adhikari, Taniya Kapoor +1Conventional Physics-Informed Neural NetworksPartial Differential Equations

  46. Reassessing Muon for Matrix Factorization

    Jul 14, 2026Ali Parviz, Gal Mishne, Alex CloningerMuonOptimizer Design

  47. Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions

    Jul 13, 2026Yongquan Shi, Zijing Ou, Shiping Wang +1Variational InferenceSurrogate Gradient

  48. Multi-dimensional training-priority weighting based on physical information propagation paths: a unified residual-weighting framework for physics-informed neural networks

    Jul 13, 2026Zhangyi Lian, Xinda Dong, Wenxuan Huo +3Conventional Physics-Informed Neural NetworksNeural Network Optimization

  49. M+Adam: Low-Precision Training via Additive-Multiplicative Optimization

    Jul 12, 2026Xiaoyuan Liang, Sebastian Loeschcke, Mads Toftrup +1Mixed-Precision QuantizationAdam

  50. Sampling on Random Subspaces under Limited Data in the Context of Exploratory Landscape Analysis

    Jul 8, 2026Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1Optimization LandscapeLow-Data Regimes

  51. An optimal control approach for neural network architecture adaptation with a posteriori error estimation

    Jul 8, 2026C G Krishnanunni, Thomas Scott, Tan Bui-ThanhNeural-Network ApproximationNeural Network Optimization

  52. Causal Optimizer Interaction Calculus: Hidden Geometric Relaxation and Identifiable Interventions

    Jul 8, 2026Zavier LiShape OptimizationAdaptive Optimizers

  53. Restricted Dynamic Geometric Complexity: Path-Space Reduction and Möbius--Jacobi Response

    Jul 8, 2026Zavier LiSpectral PreconditioningShape Optimization

  54. The Anatomy of Implicit Bias: Information Allocation in Neural Network Training

    Jul 8, 2026Zhang Gongyue, Wang Zhiyong, Liu Donghan +3Neural Network OptimizationSpectral Preconditioning

  55. Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks

    Jul 5, 2026Shokhrukh Ibragimov, Arnulf JentzenNeural Network OptimizationAdam

  56. OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers

    Jul 4, 2026Siyuan Li, Jiabao Pan, Yumou Liu +9Adaptive OptimizersMle-Bench Lite

  57. Directional Curvature from Armijo Backtracking: A Low-Cost Sharpness Probe and a Calibration-Free Learning-Rate Safeguard for Adam

    Jul 4, 2026Ashmitha R, Jörg FrochteSharpness-Aware MinimizationAdam

  58. On the Convergence of Adam, Revisited

    Jul 3, 2026Steven Heilman, Sampad MohantyAdamOptimization Convergence Analysis

  59. Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization

    Jul 2, 2026Zhenbo WangOptimal ControlGradient-Based Trajectory Optimization

  60. Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

    Jul 2, 2026Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng +4Interatomic PotentialsAdam