Awesome Numerical Analysis

Key papers

• Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks (2026)
  Mo Shakiba et al.
  5.49
• Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems (2026)
  Ryogo Tanaka et al.
  4.39
• Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental study (2026)
  Elhadji Cisse Faye et al.
  4.39
• Generative modelling powered by room-temperature polariton condensates (2026)
  Yuan Wang et al.
  4.39
• ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics (2026)
  Kirsten Odendaal et al.
  4.39
• Learning the generating functional for variance reduction in lattice QCD (2026)
  Ryan Abbott et al.
  4.39
• Machine learning enables roughness-driven inverse design of milling processes (2026)
  Hadi Bakhshan et al.
  4.39
• Enhancing Quantum Machine Learning with Anyons (2026)
  Da Zhang et al.
  4.39
• Closing the Approximation Gap in Simulation-free Latent SDEs (2026)
  Henry D. Smith et al.
  4.39
• Petrov-Galerkin Variational Physics-Informed Neural Network Framework for Two-Dimensional Singularly Perturbed Problems (2026)
  Vijay Kumar et al.
  4.39
• Learning Hybrid Biophysical Neuron Models with Neural ODEs (2026)
  Jonas Beck et al.
  4.39
• The Algebra of Units: From Buckingham's Pi-grec Theorem to Latent-Variable Learning (2026)
  Mauro Valorani
  4.39
• Sobolev Approximation by Fixed-Size Neural Networks with Arbitrary Accuracy (2026)
  Baicheng Li et al.
  4.39
• Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces (2026)
  Yahong Yang et al.
  4.39
• PH-KAN: Port-Hamiltonian Kolmogorov-Arnold Network (2026)
  Achraf El Messaoudi (UMLP et al.
  3.51
• Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model (2026)
  Siyu Chen et al.
  3.51
• Coercivity and Local Convergence of Physical Learning in Linear Circuits (2026)
  Joshua A. McGinnis et al.
  3.51
• Schattor: Schatten-family methods for deep learning optimization (2026)
  Bohao Ma et al.
  3.51
• Fantastic Pretraining Optimizers and Where to Find Them II: Hyperball Optimization (2026)
  Kaiyue Wen et al.
  3.51
• Functional Gradient Descent with Adaptive Representations (2026)
  Daniel Csillag et al.
  3.51
• Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation (2026)
  Arnulf Jentzen et al.
  2.00
• Learning Orthonormal Bases for Function Spaces (2026)
  Hamidreza Kamkari et al.
  2.00
• Predicting the Neutrino Mass Ordering Using Neural Networks (2026)
  T. J. C. Bezerra et al.
  2.00
• Adaptive Oscillatory-State Alignment for Time Series Forecasting (2026)
  Zhangyao Song et al.
  2.00
• Overcoming Rank Collapse in Feedback Alignment (2026)
  Gauthier Boeshertz et al.
  2.00
• GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations (2025)
  Milad Ramezankhani et al.
  1.33
• Nesterov Method for Asynchronous Pipeline Parallel Optimization (2025)
  Thalaiyasingam Ajanthan et al.
  1.28
• Approximating Signed Distance Fields With Sparse Ellipsoidal Radial Basis Function Networks: A Dynamic Multi-Objective Optimization Strategy (2025)
  Bobo Lian et al.
  1.28
• Continuum Transformers Perform In-Context Learning by Operator Gradient Descent (2025)
  Abhiti Mishra et al.
  1.28
• Spectral Architecture Search for Neural Network Models (2025)
  Gianluca Peri et al.
  1.22
• HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning (2025)
  Donggyun Kim et al.
  1.22
• Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization (2025)
  Ziqing Xu et al.
  1.17
• Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees (2025)
  Zecheng Zhang et al.
  1.17
• Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations (2025)
  Vladislav Trifonov et al.
  1.11
• When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach (2025)
  Qian Chen et al.
  1.06
• Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces (2025)
  Tyler Ingebrand et al.
  1.06
• RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains (2025)
  Sepehr Mousavi et al.
  1.06
• node2vec: Scalable Feature Learning for Networks (2016)
  Aditya Grover et al.
  —
• NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations (2018)
  Marco Ciccone et al.
  —
• SepNE: Bringing Separability to Network Embedding (2018)
  Ziyao Li and Liang Zhang and Guojie Song
  —
• HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking (2019)
  Shen Yan et al.
  —
• Neural Architecture Generator Optimization (2020)
  Binxin Ru et al.
  —
• Randomized Block-Diagonal Preconditioning for Parallel Learning (2020)
  Celestine Mendler-D\"unner et al.
  —
• Active Slices for Sliced Stein Discrepancy (2021)
  Wenbo Gong et al.
  —
• HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search (2021)
  Xiangzhong Luo et al.
  —
• Graph-based Neural Architecture Search with Operation Embeddings (2021)
  Michail Chatzianastasis et al.
  —
• Neighborhood-Aware Neural Architecture Search (2021)
  Xiaofang Wang et al.
  —
• Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders (2021)
  Rini Jasmine Gladstone et al.
  —
• Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization (2021)
  Jiahui Huang et al.
  —
• Interpretable Design of Reservoir Computing Networks using Realization Theory (2021)
  Wei Miao et al.
  —
• Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks (2022)
  Ziyang Zheng et al.
  —
• Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm (2022)
  Meng Liu et al.
  —
• Application Performance Modeling via Tensor Completion (2022)
  Edward Hutter and Edgar Solomonik
  —
• From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm (2022)
  Jie Chen et al.
  —
• Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation (2023)
  Henry B. Moss et al.
  —
• Flexible Channel Dimensions for Differentiable Architecture Search (2023)
  Ahmet Caner Y\"uz\"ug\"uler and Nikolaos Dimitriadis and Pascal Frossard
  —
• Flatness-Aware Minimization for Domain Generalization (2023)
  Xingxuan Zhang et al.
  —
• MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy (2023)
  Haitian Jiang et al.
  —
• Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs (2023)
  Seungjun Lee et al.
  —
• A Closed-form Solution for Weight Optimization in Fully-connected Feed-forward Neural Networks (2024)
  Slavisa Tomic et al.
  —