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Recommendations for the article Graph networks as learnable physics engines for inference and control
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index |
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visibility_off | Interaction Networks for Learning about Objects, Relations and Physics | P. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, K. Kavukcuoglu | 2016-12-01 | MAG, DBLP, ArXiv | 1293 | 71 |
visibility_off | Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases | Fábio Ferreira, Lin Shao, T. Asfour, J. Bohg | 2019-09-09 | ArXiv | 3 | 54 |
visibility_off | Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network | Seungwoong Ha, Hawoong Jeong | 2022-08-28 | ArXiv | 1 | 43 |
visibility_off | World Model as a Graph: Learning Latent Landmarks for Planning | Lunjun Zhang, Ge Yang, Bradly C. Stadie | 2020-11-25 | ArXiv | 60 | 11 |
visibility_off | Factored World Models for Zero-Shot Generalization in Robotic Manipulation | Ondrej Biza, Thomas Kipf, David Klee, Robert W. Platt, Jan-Willem van de Meent, Lawson L. S. Wong | 2022-02-10 | ArXiv | 9 | 25 |
visibility_off | Interactive Differentiable Simulation | Eric Heiden, David Millard, Hejia Zhang, G. Sukhatme | 2019-05-01 | ArXiv | 46 | 91 |
visibility_off | Visual Interaction Networks: Learning a Physics Simulator from Video | Nicholas Watters, Daniel Zoran, T. Weber, P. Battaglia, Razvan Pascanu, A. Tacchetti | 2017-06-05 | MAG, DBLP, ArXiv | 255 | 67 |
visibility_off | Contrastive Learning of Structured World Models | Thomas Kipf, Elise van der Pol, M. Welling | 2019-11-27 | ArXiv | 248 | 88 |
visibility_off | Learning Symbolic Physics with Graph Networks | M. Cranmer, Rui Xu, P. Battaglia, S. Ho | 2019-09-12 | ArXiv | 77 | 68 |
Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |