Neural Network Generalization

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21 papers in the last four weeks, up 600% on the four weeks before. 0.2% of all new papers.

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Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers
Oct 8, 2026Saeefa Rubaiyet Nowmi, Md Mahmuduzzaman Kamol, Mohammad Saidur RahmanQuantum Circuit Trainability Neural Network Generalization
Old Dominion University Norfolk, VA, USA · University of Texas at El Paso El Paso, TX, USA
HAND: A Biologically-Inspired Activation Function that Improves Generalisation and Sample Efficiency in Image Classification
Oct 8, 2026Michael W. Spratling, Heiko H. SchüttNeural Network Activation Functions Neural Network Generalization
University of Luxembourg, Department of Behavioural and Cognitive Sciences, L-4366 Esch-sur-Alzette, Luxembourg.
Dissecting Representation Structure in Vision Transformers: A Rigorous Architectural Study
Oct 8, 2026Kim-Cuc Nguyen, Ngai-Man CheungVision Transformer Neural Network Generalization
Temasek Laboratories Singapore University of Technology and Design Singapore
Do Flatter Minima Drive Better Generalization? An Algorithmic Separation in Grokking
Oct 8, 2026Mohnish HarwaniNeural Network Generalization Flat Minima
Purdue University
Generalization in Neural Networks Through the Lens of Magnitude Potential
Oct 1, 2026Sahel Torkamani, Henry Gouk, Rik SarkarNeural Network Generalization Neural Representation Geometry
School of Informatics University of Edinburgh
Why Does Train-Validation Separation Emerge? Update-Pressure Density Dynamics in Pretrained Backbones
Oct 1, 2026Yuchen Li, Mingyu Du, Zongqi Fan +2Neural Network Generalization Transfer Learning
School of Computer Science, The University of Sydney, Sydney, Australia · School of Computer Science and Engineering, UNSW Sydney, Sydney, Australia
Exact information accounting for SGD methods
Sep 30, 2026Akshay BalsubramaniNeural Network Generalization Stochastic Optimization Convergence
Transferable Graph Metanetworks
Sep 30, 2026Yuxin Ma, Adir Dayan, Yam Eitan +2Neural Network Generalization Hypernetworks
Department of Applied Mathematics and Statistics, Johns Hopkins University · Technion · NVIDIA
Forking: Sudden Overfitting Under Replay
Sep 30, 2026Shanbin Yu, Shaoyang Guo, Haoran Zhao +2Neural Network Generalization LLM Training
MetaCircle (元环智能) · Tsinghua University · Peking University +1
A Generalisation Signal Need Not Be a Model-Selection Signal
Sep 30, 2026Aditya Nagarsekar, M P Ashish Bhat, Aadi Nesarkar +5Model Selection Neural Network Generalization
Department of CS&IS, BITS Pilani, K K Birla Goa Campus · LexisNexis Legal & Professional · Center for AI and Supercomputing, Mahindra University
Reasoning with Neural Cellular Automata
Sep 28, 2026Mayalen Etcheverry, Pietro Miotti, Aidan Sirbu +7Neural Cellular Automata Neural Network Generalization
Google Paradigms of Intelligence Team · School of Computer Science, McGill University · Mila - Quebec AI Institute +4
First Learn, Then Memorize: The Spectral Bias of Diffusion Models
Sep 28, 2026Raphaël Urfin, Tony Bonnaire, Giulio Biroli +1Neural Network Generalization Memorization in Generative Models
Laboratoire de Physique de l’École normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris Cité, F-75005 Paris, France · Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France · Department of Computing Sciences, Bocconi University, Milano, Italy
Universality and Generalization of Causal Transformers Across Context Lengths
Sep 28, 2026Takashi Furuya, Maarten V. de Hoop, Gabriel PeyréNeural Network Generalization Transformer Expressivity
Doshisha University, RIKEN AIP · Rice University · CNRS, ENS, PSL Université
Neural Scaling Laws of Transformer Operator Network
Sep 27, 2026Haoran Yan, Zhongjie Shi, Yuanzhe Xi +2Neural Network Generalization Neural Network Approximation Theory
School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA · Department of Mathematics, Emory University, Atlanta, GA 30322, USA · School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
An Analytical Theory of Auxiliary Learning
Sep 24, 2026Federico Milanesio, Alessandro Ingrosso, Matteo OsellaStatistical Physics of Learning Neural Network Generalization
Department of Physics and INFN University of Turin Via Giuria 1, 10125 Turin, Italy · Donders Centre for Neuroscience Radboud University Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
A Spectral Theory of Grokking: Weight Decay induces Feature Learning
Sep 22, 2026Lenz Pracher, Pascal de Jong, Oskar Lieshaus +2Neural Network Generalization Weight Decay
Arnold-Sommerfeld-Center for Theoretical Physics, Ludwig-Maximilians-Universität München, Munich, Germany · Department of Applied Physics, Standford University, Standford, CA, USA · Department of Mathematics, University of Cambridge, Cambridge, UK
Generalized Deep Regression for Repeated Measurements
Sep 22, 2026Kexuan LiNeural Network Generalization ReLU Neural Networks
Bristol Myers Squibb
Enhanced Agriculture-informed Neural Network by Domain Knowledge
Sep 16, 2026Ci Lin, Futong Li, Rose Chong-Wu +2Neural Network Generalization
University of Ottawa, Canada
Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026
Sep 14, 2026Tristan Kirscher, Vivian Metzger, Philippe Meyer +1Neural Network Generalization U-Net
ICube, Institut Strauss · ICube Laboratory, CNRS UMR 7357, University of Strasbourg, Strasbourg, France · Institut Strauss +2
Structured Features Overfit Where Random Features Grok
Sep 14, 2026Chon-Fai Kam, Miloud Bessafi, Frederic CadetNeural Network Generalization Ridge Regression
University Paris City & University of Reunion, Paris, France; Dipartimento di Fisica e Chimica · Emilio Segr`e, Universit`a degli Studi di Palermo, Italy · EnergyLab, University of Reunion, Saint-Denis, France +2
Hierarchical Prototype Emergence in Modern Hopfield Models
Sep 14, 2026Aditya Cowsik, Adithya SriramNeural Network Generalization Hopfield Networks
Department of Physics, Stanford University, Stanford, CA 94305, USA
Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
Sep 14, 2026Erdem KoyuncuNeural Network Generalization Early-Exit Neural Networks
Department of Electrical and Computer Engineering, University of Illinois Chicago, IL, USA.
Explaining f-Divergence-Based Regularization via Local Curvature and Sharpness-Aware Minimization
Sep 8, 2026Nour Jamoussi, Marios KountourisNeural Network Generalization Sharpness-Aware Minimization
Communication Systems Department EURECOM France · Department of Computer Science and Artificial Intelligence, University of Granada, Spain
Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks
Sep 8, 2026Xiaoyu Li, Zhizhou Sha, Jiaojiao Jiang +2Neural Network Generalization ReLU Neural Networks
University of New South Wales · University of Texas at Austin · University of Sydney
A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay
Sep 7, 2026Yuqing Wang, Ioannis G. Kevrekidis, Mikhail BelkinNeural Network Generalization Neural Network Training Dynamics
Johns Hopkins University · University of California San Diego
Measuring Memory and Generalization as Separable Geometric Channels: The Topo^2 Framework
Aug 31, 2026Zhanbo Zhang, Ming Liu, Qing WangNeural Network Generalization Neural Network Memorization
Anhui iFlytek Yinglian Technology Co., Ltd.
Mapping the Emergence of Regularization-Driven Dynamics in Grokking
Aug 26, 2026Yiming Lin, Yuxuan WangNeural Network Generalization Neural Network Training Dynamics
School of Artificial Intelligence, University of Chinese Academy of Sciences
TESLA: Taylor Expansion of Sinusoidal Learnable Activations
Aug 12, 2026Daehwa Ko, Jaehyeon Kim, Seunghyun Ham +1Neural Network Generalization Neural Network Activation Functions
Korea Aerospace University, Goyang, Republic of Korea
No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
Aug 9, 2026Subhabrata Majumdar, Anand Deo, Partha Pratim Saha +1Neural Network Generalization Neural Network Robustness
Indian Institute of Management Bangalore · Independent Researcher · Indian Statistical Institute, Kolkata
Mathematical Principles and Experimental Discoveries of the Emergence of Symbolic Patterns in Artificial Neural Networks
Aug 7, 2026Quanshi Zhang, Qihan Ren, Siyu LouNeural Network Generalization Neural Network Interpretability
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China

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Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers

Saeefa Rubaiyet Nowmi, Md Mahmuduzzaman Kamol, Mohammad Saidur Rahman

Old Dominion University Norfolk, VA, USA · University of Texas at El Paso El Paso, TX, USA

Quantum Circuit Trainability Neural Network Generalization Quantum Feature Maps

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Building a quantum model involves a tradeoff: how complex the circuit should be, and how much training data it needs. Caro et al. show that models with fewer trainable gates need less training data to generalize well. Q-FLAIR shows that a quantum feature-map circuit can be grown gate-by-gate, stopping once further growth stops improving the training loss. We ask whether these two results combine into a predictable scaling law. Does Q-FLAIR's own stopping rule pick larger or smaller circuits as training data grows? Does the resulting generalization behavior track Caro et al.'s bound? We reimplement Q-FLAIR's growth mechanism faithfully, including its analytic reconstruction and exact stopping rule. We run it on full-resolution (784-pixel) MNIST 3-vs-5 classification, at five training-set sizes from N = 2000 to 10000. We then fine-tune each resulting circuit, so we can measure Caro et al.'s notion of active gates, K. We find no predictable relationship between training-set size and the circuit size Q-FLAIR converges to. Circuit size and test accuracy both vary non-monotonically with N, and seed-to-seed variance is nearly as large as any trend across N. The empirical generalization gap never exceeds Caro et al.'s bound in 14 of 15 runs, so the bound holds as a valid guarantee in those runs. But the gap correlates only weakly with the bound's value (r = 0.12). This shows that K does not explain most of the variation we observe. Why a valid guarantee can coexist with such weak predictive power remains an open question, and answering it may be necessary before circuit depth and training data size can be jointly optimized in practice.

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