{"list":"other","total":56,"rows":[{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"2002.09405","title":"Learning to Simulate Complex Physics with Graph Networks","created":"2020-02-21","cited_by_count":880},{"id":"2010.03409","title":"Learning Mesh-Based Simulation with Graph Networks","created":"2020-10-07","cited_by_count":868},{"id":"2108.13264","title":"Deep Reinforcement Learning at the Edge of the Statistical Precipice","created":"2021-08-30","cited_by_count":807},{"id":"1606.04474","title":"Learning to learn by gradient descent by gradient descent","created":"2016-06-14","cited_by_count":620},{"id":"1612.00222","title":"Interaction Networks for Learning about Objects, Relations and Physics","created":"2016-12-01","cited_by_count":509},{"id":"1807.01613","title":"Conditional Neural Processes","created":"2018-07-04","cited_by_count":426},{"id":"2202.03376","title":"Message Passing Neural PDE Solvers","created":"2022-02-07","cited_by_count":386},{"id":"1807.01622","title":"Neural Processes","created":"2018-07-04","cited_by_count":311},{"id":"1901.05761","title":"Attentive Neural Processes","created":"2019-01-17","cited_by_count":283},{"id":"1806.02817","title":"Probabilistic Model-Agnostic Meta-Learning","created":"2018-06-07","cited_by_count":256},{"id":"1806.01242","title":"Graph networks as learnable physics engines for inference and control","created":"2018-06-04","cited_by_count":244},{"id":"1810.01566","title":"Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids","created":"2018-10-03","cited_by_count":220},{"id":"2308.05732","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","created":"2023-08-10","cited_by_count":216},{"id":"1806.03836","title":"Bayesian Model-Agnostic Meta-Learning","created":"2018-06-11","cited_by_count":194},{"id":"1801.08930","title":"Recasting Gradient-Based Meta-Learning as Hierarchical Bayes","created":"2018-01-26","cited_by_count":180},{"id":"1805.09921","title":"Meta-Learning Probabilistic Inference For Prediction","created":"2018-05-24","cited_by_count":110},{"id":"2210.00612","title":"MultiScale MeshGraphNets","created":"2022-10-02","cited_by_count":98},{"id":"2201.09113","title":"Predicting Physics in Mesh-reduced Space with Temporal Attention","created":"2022-01-22","cited_by_count":84},{"id":"2306.14447","title":"RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools","created":"2023-06-26","cited_by_count":76},{"id":"1904.02642","title":"Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization","created":"2019-04-04","cited_by_count":70},{"id":"2002.01038","title":"Gated Graph Recurrent Neural Networks","created":"2020-02-03","cited_by_count":58},{"id":"2012.02788","title":"Neural Dynamic Policies for End-to-End Sensorimotor Learning","created":"2020-12-04","cited_by_count":58},{"id":"2103.01436","title":"ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations","created":"2021-03-02","cited_by_count":58},{"id":"2107.07501","title":"An End-to-End Differentiable Framework for Contact-Aware Robot Design","created":"2021-07-15","cited_by_count":54},{"id":"1906.08324","title":"The Functional Neural Process","created":"2019-06-19","cited_by_count":43},{"id":"2212.03574","title":"Learning rigid dynamics with face interaction graph networks","created":"2022-12-07","cited_by_count":43},{"id":"2202.07141","title":"Machine Learning in Aerodynamic Shape Optimization","created":"2022-02-15","cited_by_count":39},{"id":"2401.11037","title":"Equivariant Graph Neural Operator for Modeling 3D Dynamics","created":"2024-01-19","cited_by_count":38},{"id":"1802.04016","title":"Geodesic Convolutional Shape Optimization","created":"2018-02-12","cited_by_count":37},{"id":"2310.15978","title":"Graph Deep Learning for Time Series Forecasting","created":"2023-10-24","cited_by_count":26},{"id":"2406.06092","title":"Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery","created":"2024-06-10","cited_by_count":26},{"id":"2210.01531","title":"ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives","created":"2022-10-04","cited_by_count":25},{"id":"2210.09622","title":"Deep Black-Box Reinforcement Learning with Movement Primitives","created":"2022-10-18","cited_by_count":20},{"id":"2305.01122","title":"Learning Controllable Adaptive Simulation for Multi-resolution Physics","created":"2023-05-01","cited_by_count":20},{"id":"2312.12467","title":"Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer","created":"2023-12-19","cited_by_count":18},{"id":"2304.00818","title":"Swarm Reinforcement Learning For Adaptive Mesh Refinement","created":"2023-04-03","cited_by_count":17},{"id":"2312.05359","title":"Learning 3D Particle-based Simulators from RGB-D Videos","created":"2023-12-08","cited_by_count":17},{"id":"2302.11864","title":"Grounding Graph Network Simulators using Physical Sensor Observations","created":"2023-02-23","cited_by_count":15},{"id":"2109.13337","title":"DEBOSH: Deep Bayesian Shape Optimization","created":"2021-09-28","cited_by_count":14},{"id":"2401.11437","title":"Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning","created":"2024-01-21","cited_by_count":12},{"id":"2112.05068","title":"A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation","created":"2021-12-09","cited_by_count":11},{"id":"2103.02932","title":"Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects","created":"2021-03-04","cited_by_count":10},{"id":"2502.07005","title":"Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects","created":"2025-02-10","cited_by_count":10},{"id":"2506.06045","title":"Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics","created":"2025-06-06","cited_by_count":10},{"id":"2401.11985","title":"Scaling Face Interaction Graph Networks to Real World Scenes","created":"2024-01-22","cited_by_count":5},{"id":"2410.08983","title":"DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering","created":"2024-10-11","cited_by_count":4},{"id":"2505.23663","title":"AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction","created":"2025-05-29","cited_by_count":4},{"id":"2510.05874","title":"MaNGO - Adaptable Graph Network Simulators via Meta-Learning","created":"2025-10-07","cited_by_count":3},{"id":"2510.16004","title":"PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction","created":"2025-10-14","cited_by_count":2}],"next_cursor":null}