Rishabh Kabra

According to our database, Rishabh Kabra authored at least 9 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects.

Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra

CoRR, 2023

2021

Constellation: Learning relational abstractions over objects for compositional imagination.

James C. R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner

CoRR, 2021

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition.

Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt M. Botvinick, Alexander Lerchner, Christopher P. Burgess

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unsupervised Object-Based Transition Models For 3D Partially Observable Environments.

Antonia Creswell, Rishabh Kabra, Christopher P. Burgess, Murray Shanahan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PARTS: Unsupervised segmentation with slots, attention and independence maximization.

Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

AlignNet: Unsupervised Entity Alignment.

Antonia Creswell, Kyriacos Nikiforou, Oriol Vinyals, Andre Saraiva, Rishabh Kabra, Loic Matthey, Chris Burgess, Malcolm Reynolds, Richard Tanburn, Marta Garnelo, Murray Shanahan

CoRR, 2020

2019

MONet: Unsupervised Scene Decomposition and Representation.

Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew M. Botvinick, Alexander Lerchner

CoRR, 2019

An Investigation of Model-Free Planning.

Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap

Proceedings of the 36th International Conference on Machine Learning, 2019

Multi-Object Representation Learning with Iterative Variational Inference.

Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner

Proceedings of the 36th International Conference on Machine Learning, 2019