Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Burgess, Christopher P."'
Publikováno v:
Front. Phys. 12:1457543 (2024)
The recently reported compactified hyperboloidal method has found wide use in the numerical computation of quasinormal modes, with implications for fields as diverse as gravitational physics and optics. We extend this intrinsically relativistic metho
Externí odkaz:
http://arxiv.org/abs/2407.01487
Quasinormal modes (QNMs) are essential for understanding the stability and resonances of open systems, with increasing prominence in black hole physics. We present here the first study of QNMs of optical potentials. We show that solitons can support
Externí odkaz:
http://arxiv.org/abs/2309.10622
Autor:
Stocking, Kaylene C., Murez, Zak, Badrinarayanan, Vijay, Shotton, Jamie, Kendall, Alex, Tomlin, Claire, Burgess, Christopher P.
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representations have been obtained via supervised learning, but this decouples pe
Externí odkaz:
http://arxiv.org/abs/2307.07147
The Casimir effect, which predicts the emergence of an attractive force between two parallel, highly reflecting plates in vacuum, plays a vital role in various fields of physics, from quantum field theory and cosmology to nanophotonics and condensed
Externí odkaz:
http://arxiv.org/abs/2203.14385
Autor:
Whittington, James C. R., Kabra, Rishabh, Matthey, Loic, Burgess, Christopher P., Lerchner, Alexander
Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning. While there has been exciting progress with slot-based models, which learn to segment scenes into sets of objects, learning co
Externí odkaz:
http://arxiv.org/abs/2107.11153
Autor:
Kabra, Rishabh, Zoran, Daniel, Erdogan, Goker, Matthey, Loic, Creswell, Antonia, Botvinick, Matthew, Lerchner, Alexander, Burgess, Christopher P.
To help agents reason about scenes in terms of their building blocks, we wish to extract the compositional structure of any given scene (in particular, the configuration and characteristics of objects comprising the scene). This problem is especially
Externí odkaz:
http://arxiv.org/abs/2106.03849
Akademický článek
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Autor:
Duan, Sunny, Matthey, Loic, Saraiva, Andre, Watters, Nicholas, Burgess, Christopher P., Lerchner, Alexander, Higgins, Irina
Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and pr
Externí odkaz:
http://arxiv.org/abs/1905.12614
Autor:
Watters, Nicholas, Matthey, Loic, Bosnjak, Matko, Burgess, Christopher P., Lerchner, Alexander
Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms. Here we introduce a modular approach to addressing these challenges in a continuous control environment, without
Externí odkaz:
http://arxiv.org/abs/1905.09275
Autor:
Burgess, Christopher P., Matthey, Loic, Watters, Nicholas, Kabra, Rishabh, Higgins, Irina, Botvinick, Matt, Lerchner, Alexander
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simpli
Externí odkaz:
http://arxiv.org/abs/1901.11390