{"total":826,"rows":[{"id":"2103.10974","title":"Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets","created":"2021-03-19","cited_by_count":540,"area":null},{"id":"2111.11426","title":"Neural Fields in Visual Computing and Beyond","created":"2021-11-22","cited_by_count":540,"area":null},{"id":"2111.02801","title":"Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems","created":"2021-11-01","cited_by_count":222,"area":null},{"id":"2305.12827","title":"Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models","created":"2023-05-22","cited_by_count":221,"area":null},{"id":"2506.07339","title":"Real-Time Execution of Action Chunking Flow Policies","created":"2025-06-09","cited_by_count":216,"area":null},{"id":"2110.03735","title":"Adversarial Unlearning of Backdoors via Implicit Hypergradient","created":"2021-10-07","cited_by_count":166,"area":null},{"id":"2101.11517","title":"Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond","created":"2021-01-27","cited_by_count":153,"area":null},{"id":"2107.05847","title":"Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges","created":"2021-07-13","cited_by_count":145,"area":null},{"id":"2112.03052","title":"Scaling Up Influence Functions","created":"2021-12-06","cited_by_count":132,"area":null},{"id":"2307.13565","title":"Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities","created":"2023-07-25","cited_by_count":130,"area":null},{"id":"2104.12325","title":"Efficient training of physics-informed neural networks via importance sampling","created":"2021-04-26","cited_by_count":110,"area":null},{"id":"2103.02438","title":"Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design","created":"2021-03-03","cited_by_count":106,"area":null},{"id":"2201.13409","title":"A framework for bilevel optimization that enables stochastic and global variance reduction algorithms","created":"2022-01-31","cited_by_count":99,"area":null},{"id":"2110.15832","title":"CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method","created":"2021-10-29","cited_by_count":93,"area":null},{"id":"2106.05384","title":"Long-time integration of parametric evolution equations with physics-informed DeepONets","created":"2021-06-09","cited_by_count":89,"area":null},{"id":"2110.01654","title":"Improved architectures and training algorithms for deep operator networks","created":"2021-10-04","cited_by_count":86,"area":null},{"id":"2202.08587","title":"Gradients without Backpropagation","created":"2022-02-17","cited_by_count":73,"area":null},{"id":"2410.13228","title":"From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning","created":"2024-10-17","cited_by_count":73,"area":null},{"id":"2207.09238","title":"Formal Algorithms for Transformers","created":"2022-07-19","cited_by_count":72,"area":null},{"id":"2406.16218","title":"Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs","created":"2024-06-23","cited_by_count":69,"area":null},{"id":"2107.10711","title":"Physics-informed neural networks for solving Reynolds-averaged Navier$\\unicode{x2013}$Stokes equations","created":"2021-07-22","cited_by_count":68,"area":null},{"id":"2302.04107","title":"Can Physics-Informed Neural Networks beat the Finite Element Method?","created":"2023-02-08","cited_by_count":68,"area":null},{"id":"2306.15969","title":"Separable Physics-Informed Neural Networks","created":"2023-06-28","cited_by_count":66,"area":null},{"id":"2111.14580","title":"Amortized Implicit Differentiation for Stochastic Bilevel Optimization","created":"2021-11-29","cited_by_count":63,"area":null},{"id":"2109.09338","title":"Learning in Sinusoidal Spaces with Physics-Informed Neural Networks","created":"2021-09-20","cited_by_count":62,"area":null},{"id":"2111.02329","title":"Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods","created":"2021-11-03","cited_by_count":61,"area":null},{"id":"2106.14623","title":"Polyconvex anisotropic hyperelasticity with neural networks","created":"2021-06-20","cited_by_count":55,"area":null},{"id":"2104.07359","title":"Robust Generalised Bayesian Inference for Intractable Likelihoods","created":"2021-04-15","cited_by_count":53,"area":null},{"id":"2106.01413","title":"Rectangular Flows for Manifold Learning","created":"2021-06-02","cited_by_count":50,"area":null},{"id":"2206.10745","title":"Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning","created":"2022-06-21","cited_by_count":44,"area":null},{"id":"2103.12032","title":"Tangent Space Backpropagation for 3D Transformation Groups","created":"2021-03-22","cited_by_count":40,"area":null},{"id":"2407.17611","title":"Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks","created":"2024-07-24","cited_by_count":38,"area":null},{"id":"2408.09446","title":"Parameterized Physics-informed Neural Networks for Parameterized PDEs","created":"2024-08-18","cited_by_count":38,"area":null},{"id":"2102.07870","title":"Momentum Residual Neural Networks","created":"2021-02-15","cited_by_count":37,"area":null},{"id":"2111.08823","title":"Meta-Auto-Decoder for Solving Parametric Partial Differential Equations","created":"2021-11-15","cited_by_count":36,"area":null},{"id":"2203.13760","title":"JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows","created":"2022-03-25","cited_by_count":36,"area":null},{"id":"2207.02542","title":"Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems","created":"2022-07-06","cited_by_count":36,"area":null},{"id":"2301.05032","title":"Online Hyperparameter Optimization for Class-Incremental Learning","created":"2023-01-11","cited_by_count":36,"area":null},{"id":"2107.10991","title":"A novel meta-learning initialization method for physics-informed neural networks","created":"2021-07-23","cited_by_count":35,"area":null},{"id":"2405.03658","title":"A review on data-driven constitutive laws for solids","created":"2024-05-06","cited_by_count":35,"area":null},{"id":"2111.09708","title":"A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration","created":"2021-11-18","cited_by_count":34,"area":null},{"id":"2212.03130","title":"Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems","created":"2022-12-06","cited_by_count":34,"area":null},{"id":"2206.13103","title":"A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method","created":"2022-06-27","cited_by_count":33,"area":null},{"id":"2102.11026","title":"High-order Differentiable Autoencoder for Nonlinear Model Reduction","created":"2021-02-19","cited_by_count":32,"area":null},{"id":"2104.00615","title":"Physics-informed neural networks for the shallow-water equations on the sphere","created":"2021-04-01","cited_by_count":32,"area":null},{"id":"2210.10837","title":"On Learning Fairness and Accuracy on Multiple Subgroups","created":"2022-10-19","cited_by_count":32,"area":null},{"id":"2309.07835","title":"Learning to Warm-Start Fixed-Point Optimization Algorithms","created":"2023-09-14","cited_by_count":32,"area":null},{"id":"2402.15332","title":"Position: Categorical Deep Learning is an Algebraic Theory of All Architectures","created":"2024-02-23","cited_by_count":32,"area":null},{"id":"2109.12482","title":"Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data","created":"2021-09-26","cited_by_count":31,"area":null},{"id":"2111.01495","title":"Constructing Neural Network-Based Models for Simulating Dynamical Systems","created":"2021-11-02","cited_by_count":31,"area":null},{"id":"2111.05685","title":"Efficient Neural Network Training via Forward and Backward Propagation Sparsification","created":"2021-11-10","cited_by_count":30,"area":null},{"id":"2205.06664","title":"NN-EUCLID: deep-learning hyperelasticity without stress data","created":"2022-05-04","cited_by_count":30,"area":null},{"id":"2102.03013","title":"Fast and Memory Efficient Differentially Private-SGD via JL Projections","created":"2021-02-05","cited_by_count":29,"area":null},{"id":"2103.06525","title":"Implicit energy regularization of neural ordinary-differential-equation control","created":"2021-03-11","cited_by_count":29,"area":null},{"id":"2207.13513","title":"Learning with Combinatorial Optimization Layers: a Probabilistic Approach","created":"2022-07-27","cited_by_count":29,"area":null},{"id":"2209.02681","title":"How important are activation functions in regression and classification? A survey, performance comparison, and future directions","created":"2022-09-06","cited_by_count":29,"area":null},{"id":"2210.08572","title":"Automatic Differentiation of Programs with Discrete Randomness","created":"2022-10-16","cited_by_count":29,"area":null},{"id":"2102.07976","title":"A General Descent Aggregation Framework for Gradient-based Bi-level Optimization","created":"2021-02-16","cited_by_count":28,"area":null},{"id":"2109.00817","title":"NASI: Label- and Data-agnostic Neural Architecture Search at Initialization","created":"2021-09-02","cited_by_count":28,"area":null},{"id":"2204.07172","title":"Diagnosing and Fixing Manifold Overfitting in Deep Generative Models","created":"2022-04-14","cited_by_count":28,"area":null},{"id":"2105.00862","title":"Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks","created":"2021-05-03","cited_by_count":27,"area":null},{"id":"2205.14612","title":"Do Residual Neural Networks discretize Neural Ordinary Differential Equations?","created":"2022-05-29","cited_by_count":27,"area":null},{"id":"2303.08011","title":"Model scale versus domain knowledge in statistical forecasting of chaotic systems","created":"2023-03-13","cited_by_count":27,"area":null},{"id":"2103.14779","title":"Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks","created":"2021-03-27","cited_by_count":26,"area":null},{"id":"2106.01138","title":"Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting","created":"2021-06-02","cited_by_count":26,"area":null},{"id":"2110.14121","title":"On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements","created":"2021-10-27","cited_by_count":26,"area":null},{"id":"2306.06968","title":"Can Forward Gradient Match Backpropagation?","created":"2023-06-12","cited_by_count":26,"area":null},{"id":"2107.10879","title":"Discovering Sparse Interpretable Dynamics from Partial Observations","created":"2021-07-22","cited_by_count":25,"area":null},{"id":"2110.13297","title":"Fast PDE-constrained optimization via self-supervised operator learning","created":"2021-10-25","cited_by_count":25,"area":null},{"id":"2211.05567","title":"Partial Differential Equations Meet Deep Neural Networks: A Survey","created":"2022-10-27","cited_by_count":25,"area":null},{"id":"2111.00998","title":"PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning","created":"2021-11-01","cited_by_count":24,"area":null},{"id":"2201.12354","title":"Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning","created":"2022-01-28","cited_by_count":24,"area":null},{"id":"2211.07377","title":"Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing","created":"2022-11-14","cited_by_count":24,"area":null},{"id":"2203.13648","title":"On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks","created":"2022-03-25","cited_by_count":23,"area":null},{"id":"2310.00697","title":"Learning How to Propagate Messages in Graph Neural Networks","created":"2023-10-01","cited_by_count":23,"area":null},{"id":"2105.01637","title":"Implicit differentiation for fast hyperparameter selection in non-smooth convex learning","created":"2021-05-04","cited_by_count":22,"area":null},{"id":"2207.12800","title":"PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers","created":"2022-07-26","cited_by_count":22,"area":null},{"id":"2208.13825","title":"Differentiable Programming for Earth System Modeling","created":"2022-08-29","cited_by_count":22,"area":null},{"id":"2109.10793","title":"Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators","created":"2021-09-22","cited_by_count":21,"area":null},{"id":"2203.14827","title":"Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy","created":"2022-03-28","cited_by_count":21,"area":null},{"id":"2204.01701","title":"QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration","created":"2022-04-01","cited_by_count":21,"area":null},{"id":"2302.13397","title":"Efficient physics-informed neural networks using hash encoding","created":"2023-02-26","cited_by_count":21,"area":null},{"id":"2105.09384","title":"Graph Sanitation with Application to Node Classification","created":"2021-05-19","cited_by_count":20,"area":null},{"id":"2106.05587","title":"A Discontinuity Capturing Shallow Neural Network for Elliptic Interface Problems","created":"2021-06-10","cited_by_count":20,"area":null},{"id":"2212.04971","title":"PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data","created":"2022-12-09","cited_by_count":20,"area":null},{"id":"2303.14878","title":"GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs","created":"2023-03-27","cited_by_count":20,"area":null},{"id":"2306.00144","title":"Mechanic: A Learning Rate Tuner","created":"2023-05-31","cited_by_count":20,"area":null},{"id":"2403.20233","title":"Functional Bilevel Optimization for Machine Learning","created":"2024-03-29","cited_by_count":20,"area":null},{"id":"2510.19944","title":"Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets","created":"2025-10-22","cited_by_count":20,"area":null},{"id":"2302.00807","title":"Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils","created":"2023-02-02","cited_by_count":19,"area":null},{"id":"2303.07127","title":"Improving physics-informed neural networks with meta-learned optimization","created":"2023-03-13","cited_by_count":19,"area":null},{"id":"2306.14969","title":"On the Sample Complexity of Quantum Boltzmann Machine Learning","created":"2023-06-26","cited_by_count":19,"area":null},{"id":"2106.10314","title":"Differentiable Particle Filtering without Modifying the Forward Pass","created":"2021-06-18","cited_by_count":18,"area":null},{"id":"2110.13330","title":"Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks","created":"2021-10-26","cited_by_count":18,"area":null},{"id":"2205.00578","title":"Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials","created":"2022-05-01","cited_by_count":18,"area":null},{"id":"2207.14671","title":"High Dynamic Range and Super-Resolution from Raw Image Bursts","created":"2022-07-29","cited_by_count":18,"area":null},{"id":"2304.11247","title":"Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes","created":"2023-04-21","cited_by_count":18,"area":null},{"id":"2308.12716","title":"Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks","created":"2023-08-24","cited_by_count":18,"area":null},{"id":"2312.09597","title":"Deep Generative Models for Detector Signature Simulation: A Taxonomic Review","created":"2023-12-15","cited_by_count":18,"area":null},{"id":"2409.04538","title":"Operator Learning with Gaussian Processes","created":"2024-09-06","cited_by_count":18,"area":null}],"next_cursor":"100"}