Under review as a conference paper at ICLR 2017 D ISCOVERING OBJECTS AND THEIR RELATIONS FROM ENTANGLED SCENE REPRESENTATIONS
By David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Timothy P. Lillicrap, Peter W. Battaglia. 2016
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yoshua Bengio
- Sergey Levine
- Trevor Darrell
- Dacheng Tao
- Ruslan Salakhutdinov
- Abhinav Kumar Gupta
- Zhe Gan
- Quoc V. Le
Citations in the corpus
- Recurrent Independent Mechanisms (2019, background): “…multi-object scenes, and motion-capture data, and multiagent systems (Scarselli et al., 2008; Bronstein et al., 2017; Watters et al., 2017; Raposo et al., 2017; Santoro et al., 2017; Gilmer et al., 2017; Van Steenkiste et al., 2018; Kipf et al., 2018; Battaglia et al., 2018; Tacchetti et…”
- Relational Deep Reinforcement Learning (2018, background): “This approach builds off previous work suggesting that relational computations needn’t necessarily be biased by entities’ spatial proximity [20, 10, 21, 11, 13], and may also profit from iterative structured reasoning [22, 23, 24, 25].”
- Deformable ConvNets V2: More Deformable, Better Results (2018, background): “Relation Networks and Attention Modules are first proposed in natural language processing [13, 14, 3, 35] and physical system modeling [2, 37, 22, 34, 9, 31].”
- Deep reinforcement learning with relational inductive biases (2018, background): “This provides a powerful capacity for combinatorial generalization, where their learned building blocks can be composed to represent and reason about novel scenarios (Battaglia et al., 2016; Raposo et al., 2017; Dai et al., 2017; Wang et al., 2018; Selsam et al., 2018; Hamrick et al., 2018; Kipf et ”