Feedforward Neural Networks

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2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

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  1. How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks

    May 22, 2026Dong-Won LimSample ComplexityInverse Kinematics

    The University of Suwon, Hwaseong 18323, Republic of Korea

  2. Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks

    May 21, 2026Margalit Glasgow, Joan BrunaShallow Neural NetworksMean-Field Theory

    Massachusetts Institute of Technology · Courant Institute School of Mathematics, Computing and Data Science, New York University

  3. Approximation Theory for Neural Networks: Old and New

    May 20, 2026Soumendu Sundar Mukherjee, Himasish TalukdarUniversal ApproximationNeural Network Approximation Theory

    Statistics and Mathematics Unit, Indian Statistical Institute, Kolkata

  4. Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity

    May 8, 2026Anastasis Kratsios, Gregory Cousins, Haitz Sáez de Ocáriz Borde +2Neural Network GeneralizationPAC Learning

    Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada · University of Cambridge, United Kingdom · Graduate School of Engineering, The University of Tokyo, Tokyo, Japan +1

  5. Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks

    May 5, 2026Stephen Tivenan, Indranil Sahoo, Yanjun QianUncertainty QuantificationFeedforward Neural Networks

    Department of Statistical Sciences and Operations Research, Virginia Commonwealth University

  6. Communication Dynamics Neural Networks: FFT-Diagonalized Layers for Improved Hessian Conditioning at Reduced Parameter Count

    May 4, 2026Lurong PanNeural Network OptimizationFeedforward Neural Networks

  7. Diffusion Operator Geometry of Feedforward Representations

    May 1, 2026Kanishka ReddyNeural Representation GeometryFeedforward Neural Networks

    Department of Applied Mathematics University of Washington Seattle, WA

  8. Random Cloud: Finding Minimal Neural Architectures Without Training

    Apr 29, 2026Javier Gil BlázquezNeural Architecture SearchFeedforward Neural Networks

  9. Verification of Neural Networks (Lecture Notes)

    Apr 28, 2026Benedikt BolligNeural Network VerificationFeedforward Neural Networks

    Universit´e Paris-Saclay, CNRS, ENS Paris-Saclay, LMF

  10. Complete Identification of Deep ReLU Networks through Łukasiewicz Logic

    Jan 30, 2026Yani Zhang, Helmut BölcskeiParameter IdentifiabilityReLU Neural Networks

    Chair for Mathematical Information Science, ETH Zürich

  11. A Computational Tropical Geometry Framework for Neural Networks

    May 30, 2024Paul Lezeau, Thomas Walker, Yueqi Cao +2Feedforward Neural NetworksNeural Network Expressivity

    Department of Mathematics, Imperial College London, UK · London School of Geometry and Number Theory, UK · Department of Electrical and Computer Engineering, Rice University, USA +2