{"list":"other","total":69,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1512.03012","title":"ShapeNet: An Information-Rich 3D Model Repository","created":"2015-12-09","cited_by_count":2897},{"id":"1901.05103","title":"DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation","created":"2019-01-16","cited_by_count":2689},{"id":"1812.03828","title":"Occupancy Networks: Learning 3D Reconstruction in Function Space","created":"2018-12-10","cited_by_count":2041},{"id":"1812.02822","title":"Learning Implicit Fields for Generative Shape Modeling","created":"2018-12-06","cited_by_count":1060},{"id":"1606.03657","title":"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets","created":"2016-06-12","cited_by_count":860},{"id":"2007.08501","title":"Accelerating 3D Deep Learning with PyTorch3D","created":"2020-07-16","cited_by_count":784},{"id":"1801.09847","title":"Open3D: A Modern Library for 3D Data Processing","created":"2018-01-30","cited_by_count":721},{"id":"1906.01563","title":"Hamiltonian Neural Networks","created":"2019-06-04","cited_by_count":712},{"id":"1912.07372","title":"Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision","created":"2019-12-16","cited_by_count":700},{"id":"1905.00889","title":"Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines","created":"2019-05-02","cited_by_count":672},{"id":"1804.01654","title":"Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images","created":"2018-04-05","cited_by_count":651},{"id":"1802.05384","title":"AtlasNet: A Papier-M\\^ach\\'e Approach to Learning 3D Surface Generation","created":"2018-02-15","cited_by_count":539},{"id":"1911.01429","title":"The frontier of simulation-based inference","created":"2019-11-04","cited_by_count":502},{"id":"1711.07566","title":"Neural 3D Mesh Renderer","created":"2017-11-20","cited_by_count":437},{"id":"2101.10994","title":"Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes","created":"2021-01-26","cited_by_count":426},{"id":"1904.01786","title":"Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning","created":"2019-04-03","cited_by_count":420},{"id":"2003.04630","title":"Lagrangian Neural Networks","created":"2020-03-10","cited_by_count":398},{"id":"1905.10711","title":"DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction","created":"2019-05-26","cited_by_count":333},{"id":"1703.09312","title":"Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics","created":"2017-03-27","cited_by_count":264},{"id":"1910.00935","title":"DiffTaichi: Differentiable Programming for Physical Simulation","created":"2019-10-01","cited_by_count":258},{"id":"1908.01210","title":"Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer","created":"2019-08-03","cited_by_count":221},{"id":"1810.01566","title":"Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids","created":"2018-10-03","cited_by_count":220},{"id":"2003.01460","title":"Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction","created":"2020-03-03","cited_by_count":195},{"id":"1909.12077","title":"Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control","created":"2019-09-26","cited_by_count":184},{"id":"1606.07419","title":"Learning to Poke by Poking: Experiential Learning of Intuitive Physics","created":"2016-06-23","cited_by_count":154},{"id":"1909.13334","title":"Symplectic Recurrent Neural Networks","created":"2019-09-29","cited_by_count":150},{"id":"1909.13789","title":"Hamiltonian Generative Networks","created":"2019-09-30","cited_by_count":137},{"id":"1612.00341","title":"A Compositional Object-Based Approach to Learning Physical Dynamics","created":"2016-12-01","cited_by_count":119},{"id":"1611.01652","title":"A Differentiable Physics Engine for Deep Learning in Robotics","created":"2016-11-05","cited_by_count":108},{"id":"1909.12790","title":"Hamiltonian Graph Networks with ODE Integrators","created":"2019-09-27","cited_by_count":108},{"id":"1912.05237","title":"Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis","created":"2019-12-11","cited_by_count":104},{"id":"2010.09125","title":"Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering","created":"2020-10-18","cited_by_count":101},{"id":"1810.01054","title":"ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics","created":"2018-10-02","cited_by_count":98},{"id":"1911.05063","title":"Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research","created":"2019-11-12","cited_by_count":98},{"id":"1706.01433","title":"Visual Interaction Networks","created":"2017-06-05","cited_by_count":95},{"id":"1906.01728","title":"BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators","created":"2019-06-04","cited_by_count":85},{"id":"2011.01437","title":"Learning Deformable Tetrahedral Meshes for 3D Reconstruction","created":"2020-11-03","cited_by_count":85},{"id":"2007.02168","title":"Scalable Differentiable Physics for Learning and Control","created":"2020-07-04","cited_by_count":73},{"id":"1607.00662","title":"Unsupervised Learning of 3D Structure from Images","created":"2016-07-03","cited_by_count":67},{"id":"1906.03853","title":"DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions","created":"2019-06-10","cited_by_count":63},{"id":"1806.06094","title":"SPNets: Differentiable Fluid Dynamics for Deep Neural Networks","created":"2018-06-15","cited_by_count":62},{"id":"1511.07404","title":"Learning Visual Predictive Models of Physics for Playing Billiards","created":"2015-11-23","cited_by_count":58},{"id":"2004.13664","title":"Visual Grounding of Learned Physical Models","created":"2020-04-28","cited_by_count":55},{"id":"1812.10972","title":"Reasoning About Physical Interactions with Object-Oriented Prediction and Planning","created":"2018-12-28","cited_by_count":51},{"id":"1606.02378","title":"SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks","created":"2016-06-08","cited_by_count":49},{"id":"1806.06575","title":"RenderNet: A deep convolutional network for differentiable rendering from 3D shapes","created":"2018-06-18","cited_by_count":42},{"id":"2003.12045","title":"Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects","created":"2020-03-26","cited_by_count":40},{"id":"1901.11461","title":"GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects","created":"2019-01-31","cited_by_count":27},{"id":"1511.04048","title":"Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images","created":"2015-11-12","cited_by_count":26},{"id":"1901.01535","title":"RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials","created":"2019-01-06","cited_by_count":26},{"id":"1603.05600","title":"\"What happens if...\" Learning to Predict the Effect of Forces in Images","created":"2016-03-17","cited_by_count":23},{"id":"2001.08861","title":"Encoding Physical Constraints in Differentiable Newton-Euler Algorithm","created":"2020-01-24","cited_by_count":23},{"id":"2006.12682","title":"Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction","created":"2020-06-23","cited_by_count":18},{"id":"1905.10706","title":"Interactive Differentiable Simulation","created":"2019-05-26","cited_by_count":17},{"id":"1809.04696","title":"Geometric Image Synthesis","created":"2018-09-12","cited_by_count":16},{"id":"1701.04395","title":"Linear Matrix Inequalities for Physically-Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distribution","created":"2017-01-16","cited_by_count":12},{"id":"2005.05456","title":"Learning to Slide Unknown Objects with Differentiable Physics Simulations","created":"2020-05-11","cited_by_count":12},{"id":"1802.09987","title":"Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation","created":"2018-02-27","cited_by_count":7},{"id":"1805.05086","title":"Unsupervised Intuitive Physics from Visual Observations","created":"2018-05-14","cited_by_count":7},{"id":"2004.13859","title":"A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines","created":"2020-04-28","cited_by_count":7},{"id":"1703.00247","title":"Learning A Physical Long-term Predictor","created":"2017-03-01","cited_by_count":5},{"id":"1711.02712","title":"Tangent: Automatic Differentiation Using Source Code Transformation in Python","created":"2017-11-07","cited_by_count":4},{"id":"1808.04931","title":"Neural Material: Learning Elastic Constitutive Material and Damping Models from Sparse Data","created":"2018-08-15","cited_by_count":3},{"id":"1908.06217","title":"Neural Re-Simulation for Generating Bounces in Single Images","created":"2019-08-17","cited_by_count":3},{"id":"2005.05410","title":"Identifying Mechanical Models through Differentiable Simulations","created":"2020-05-11","cited_by_count":1}],"next_cursor":null}