{"total":375,"rows":[{"id":"2106.10934","title":"GRAND: Graph Neural Diffusion","created":"2021-06-21","cited_by_count":258,"area":null},{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252,"area":null},{"id":"2110.11773","title":"Sinkformers: Transformers with Doubly Stochastic Attention","created":"2021-10-22","cited_by_count":138,"area":null},{"id":"2209.12951","title":"Liquid Structural State-Space Models","created":"2022-09-26","cited_by_count":136,"area":null},{"id":"2110.13040","title":"Neural Flows: Efficient Alternative to Neural ODEs","created":"2021-10-25","cited_by_count":101,"area":null},{"id":"2412.06329","title":"Normalizing Flows are Capable Generative Models","created":"2024-12-09","cited_by_count":91,"area":null},{"id":"2103.12803","title":"JFB: Jacobian-Free Backpropagation for Implicit Networks","created":"2021-03-23","cited_by_count":86,"area":null},{"id":"2110.09443","title":"Beltrami Flow and Neural Diffusion on Graphs","created":"2021-10-18","cited_by_count":84,"area":null},{"id":"2111.05177","title":"On Training Implicit Models","created":"2021-11-09","cited_by_count":75,"area":null},{"id":"2106.14342","title":"Stabilizing Equilibrium Models by Jacobian Regularization","created":"2021-06-28","cited_by_count":72,"area":null},{"id":"2202.08408","title":"Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs","created":"2022-02-17","cited_by_count":72,"area":null},{"id":"2107.08024","title":"Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems","created":"2021-07-16","cited_by_count":67,"area":null},{"id":"2106.13898","title":"Closed-form Continuous-time Neural Models","created":"2021-06-25","cited_by_count":65,"area":null},{"id":"2204.08442","title":"Deep Equilibrium Optical Flow Estimation","created":"2022-04-18","cited_by_count":64,"area":null},{"id":"2403.17844","title":"Mechanistic Design and Scaling of Hybrid Architectures","created":"2024-03-26","cited_by_count":59,"area":null},{"id":"2103.12413","title":"Neural ODE Processes","created":"2021-03-23","cited_by_count":57,"area":null},{"id":"2106.12619","title":"Machine learning structure preserving brackets for forecasting irreversible processes","created":"2021-06-23","cited_by_count":52,"area":null},{"id":"2106.02875","title":"Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression","created":"2021-06-05","cited_by_count":48,"area":null},{"id":"2107.01272","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","created":"2021-07-02","cited_by_count":44,"area":null},{"id":"2202.02526","title":"LyaNet: A Lyapunov Framework for Training Neural ODEs","created":"2022-02-05","cited_by_count":44,"area":null},{"id":"2102.04668","title":"MALI: A memory efficient and reverse accurate integrator for Neural ODEs","created":"2021-02-09","cited_by_count":42,"area":null},{"id":"2103.14755","title":"Survival Regression with Proper Scoring Rules and Monotonic Neural Networks","created":"2021-03-26","cited_by_count":42,"area":null},{"id":"2110.04840","title":"Heavy Ball Neural Ordinary Differential Equations","created":"2021-10-10","cited_by_count":42,"area":null},{"id":"2112.06351","title":"Neural Point Process for Learning Spatiotemporal Event Dynamics","created":"2021-12-12","cited_by_count":42,"area":null},{"id":"2102.04764","title":"Continuous-Time Model-Based Reinforcement Learning","created":"2021-02-09","cited_by_count":41,"area":null},{"id":"2103.13582","title":"Learning Dynamic Alignment via Meta-filter for Few-shot Learning","created":"2021-03-25","cited_by_count":41,"area":null},{"id":"2109.06407","title":"Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling","created":"2021-09-14","cited_by_count":41,"area":null},{"id":"2102.06559","title":"Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations","created":"2021-02-12","cited_by_count":38,"area":null},{"id":"2203.08652","title":"Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow","created":"2022-03-16","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":"2402.14989","title":"Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data","created":"2024-02-22","cited_by_count":36,"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":"2109.01876","title":"Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting","created":"2021-09-04","cited_by_count":29,"area":null},{"id":"2105.14969","title":"OCT-GAN: Neural ODE-based Conditional Tabular GANs","created":"2021-05-31","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":"2304.00600","title":"Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models","created":"2023-04-02","cited_by_count":27,"area":null},{"id":"2102.10801","title":"Neural Delay Differential Equations","created":"2021-02-22","cited_by_count":26,"area":null},{"id":"2206.04843","title":"Neural Laplace: Learning diverse classes of differential equations in the Laplace domain","created":"2022-06-10","cited_by_count":24,"area":null},{"id":"2302.09639","title":"An overview of differentiable particle filters for data-adaptive sequential Bayesian inference","created":"2023-02-19","cited_by_count":23,"area":null},{"id":"2204.08771","title":"EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting","created":"2022-04-19","cited_by_count":22,"area":null},{"id":"2205.13051","title":"Online Deep Equilibrium Learning for Regularization by Denoising","created":"2022-05-25","cited_by_count":22,"area":null},{"id":"2210.01802","title":"Alternating Differentiation for Optimization Layers","created":"2022-10-03","cited_by_count":22,"area":null},{"id":"2311.04128","title":"Generative learning for nonlinear dynamics","created":"2023-11-07","cited_by_count":22,"area":null},{"id":"2108.06208","title":"LT-OCF: Learnable-Time ODE-based Collaborative Filtering","created":"2021-08-08","cited_by_count":21,"area":null},{"id":"2206.07335","title":"On Numerical Integration in Neural Ordinary Differential Equations","created":"2022-06-15","cited_by_count":21,"area":null},{"id":"2309.01213","title":"Implicit regularization of deep residual networks towards neural ODEs","created":"2023-09-03","cited_by_count":20,"area":null},{"id":"2310.16975","title":"Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference","created":"2023-10-25","cited_by_count":20,"area":null},{"id":"2502.09885","title":"Comprehensive Review of Neural Differential Equations for Time Series Analysis","created":"2025-02-14","cited_by_count":20,"area":null},{"id":"2206.03111","title":"Medical Image Registration via Neural Fields","created":"2022-06-07","cited_by_count":19,"area":null},{"id":"2102.01577","title":"Policy Analysis using Synthetic Controls in Continuous-Time","created":"2021-02-02","cited_by_count":18,"area":null},{"id":"2105.13205","title":"Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design","created":"2021-05-27","cited_by_count":18,"area":null},{"id":"2107.00961","title":"ResIST: Layer-Wise Decomposition of ResNets for Distributed Training","created":"2021-07-02","cited_by_count":18,"area":null},{"id":"2206.00860","title":"Self-Consistency of the Fokker-Planck Equation","created":"2022-06-02","cited_by_count":18,"area":null},{"id":"2109.06786","title":"Multiple shooting for training neural differential equations on time series","created":"2021-09-14","cited_by_count":17,"area":null},{"id":"2110.14296","title":"Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems","created":"2021-10-27","cited_by_count":17,"area":null},{"id":"2212.02483","title":"TIDE: Time Derivative Diffusion for Deep Learning on Graphs","created":"2022-12-05","cited_by_count":17,"area":null},{"id":"2404.17099","title":"Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND","created":"2024-04-26","cited_by_count":17,"area":null},{"id":"2406.03164","title":"Topological Neural Networks go Persistent, Equivariant, and Continuous","created":"2024-06-05","cited_by_count":17,"area":null},{"id":"2105.08644","title":"Ab-initio study of interacting fermions at finite temperature with neural canonical transformation","created":"2021-05-18","cited_by_count":16,"area":null},{"id":"2105.12245","title":"Scaling Properties of Deep Residual Networks","created":"2021-05-25","cited_by_count":16,"area":null},{"id":"2402.13077","title":"Mechanistic Neural Networks for Scientific Machine Learning","created":"2024-02-20","cited_by_count":16,"area":null},{"id":"2405.20799","title":"Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching","created":"2024-05-31","cited_by_count":16,"area":null},{"id":"2105.14953","title":"ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations","created":"2021-05-31","cited_by_count":15,"area":null},{"id":"2106.12430","title":"Beyond Predictions in Neural ODEs: Identification and Interventions","created":"2021-06-23","cited_by_count":15,"area":null},{"id":"2111.13236","title":"Joint inference and input optimization in equilibrium networks","created":"2021-11-25","cited_by_count":15,"area":null},{"id":"2306.09739","title":"Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints","created":"2023-06-16","cited_by_count":15,"area":null},{"id":"2311.03260","title":"From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach","created":"2023-11-06","cited_by_count":15,"area":null},{"id":"2411.06311","title":"When are dynamical systems learned from time series data statistically accurate?","created":"2024-11-09","cited_by_count":15,"area":null},{"id":"2106.10820","title":"Stateful ODE-Nets using Basis Function Expansions","created":"2021-06-21","cited_by_count":14,"area":null},{"id":"2106.12718","title":"Sparse Flows: Pruning Continuous-depth Models","created":"2021-06-24","cited_by_count":13,"area":null},{"id":"2108.02318","title":"Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations","created":"2021-07-23","cited_by_count":13,"area":null},{"id":"2207.06531","title":"Reachability Analysis of a General Class of Neural Ordinary Differential Equations","created":"2022-07-13","cited_by_count":13,"area":null},{"id":"2402.07594","title":"Zero-shot Imputation with Foundation Inference Models for Dynamical Systems","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2502.02480","title":"Stable Port-Hamiltonian Neural Networks","created":"2025-02-04","cited_by_count":13,"area":null},{"id":"2109.03069","title":"Sequential Diagnosis Prediction with Transformer and Ontological Representation","created":"2021-09-07","cited_by_count":12,"area":null},{"id":"2401.17992","title":"Multilinear Operator Networks","created":"2024-01-31","cited_by_count":12,"area":null},{"id":"2105.12152","title":"Density estimation on low-dimensional manifolds: an inflation-deflation approach","created":"2021-05-25","cited_by_count":11,"area":null},{"id":"2106.03500","title":"Density estimation on smooth manifolds with normalizing flows","created":"2021-06-07","cited_by_count":11,"area":null},{"id":"2110.08649","title":"Equivariant Finite Normalizing Flows","created":"2021-10-16","cited_by_count":11,"area":null},{"id":"2210.04763","title":"On the Forward Invariance of Neural ODEs","created":"2022-10-10","cited_by_count":11,"area":null},{"id":"2310.05227","title":"Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology","created":"2023-10-08","cited_by_count":11,"area":null},{"id":"2402.01231","title":"Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations","created":"2024-02-02","cited_by_count":11,"area":null},{"id":"2506.05480","title":"ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting","created":"2025-06-05","cited_by_count":11,"area":null},{"id":"2106.15580","title":"Continuous Latent Process Flows","created":"2021-06-29","cited_by_count":10,"area":null},{"id":"2110.07034","title":"How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies","created":"2021-10-13","cited_by_count":10,"area":null},{"id":"2203.11556","title":"VQ-Flows: Vector Quantized Local Normalizing Flows","created":"2022-03-22","cited_by_count":10,"area":null},{"id":"2209.10091","title":"Variational Inference for Infinitely Deep Neural Networks","created":"2022-09-21","cited_by_count":10,"area":null},{"id":"2210.12375","title":"torchode: A Parallel ODE Solver for PyTorch","created":"2022-10-22","cited_by_count":10,"area":null},{"id":"2210.14672","title":"Sparsity in Continuous-Depth Neural Networks","created":"2022-10-26","cited_by_count":10,"area":null},{"id":"2302.13262","title":"Modulated Neural ODEs","created":"2023-02-26","cited_by_count":10,"area":null},{"id":"2304.12707","title":"Lyapunov-Stable Deep Equilibrium Models","created":"2023-04-25","cited_by_count":10,"area":null},{"id":"2503.07596","title":"Denoising Hamiltonian Network for Physical Reasoning","created":"2025-03-10","cited_by_count":10,"area":null},{"id":"2102.04801","title":"Automatic variational inference with cascading flows","created":"2021-02-09","cited_by_count":9,"area":null},{"id":"2103.04913","title":"The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning","created":"2021-03-08","cited_by_count":9,"area":null},{"id":"2105.14070","title":"Accelerating Neural ODEs Using Model Order Reduction","created":"2021-05-28","cited_by_count":9,"area":null},{"id":"2108.04993","title":"LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising","created":"2021-08-11","cited_by_count":9,"area":null},{"id":"2307.07693","title":"Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging","created":"2023-07-15","cited_by_count":9,"area":null},{"id":"2401.04331","title":"Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study","created":"2024-01-09","cited_by_count":9,"area":null},{"id":"2401.09902","title":"Interplay between depth and width for interpolation in neural ODEs","created":"2024-01-18","cited_by_count":9,"area":null},{"id":"2405.02154","title":"Neural Context Flows for Meta-Learning of Dynamical Systems","created":"2024-05-03","cited_by_count":9,"area":null}],"next_cursor":"100"}