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David Philipona

dblp:20/4437 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 77% Representation and self-supervised learning · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot learning
sensorimotor learning
0.012003
Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors · NIPS 2003
Machine learning › Representation and self-supervised learning
multimodal representation learning
0.012003
Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors · NIPS 2003

Methods — techniques the papers use, named apart from their topics

simulation · 0.0sensorimotor analysis · 0.0
YearPublicationVenuePosition
2004 Model of granular layer encoding in the cerebellum
David Philipona, Olivier J. M. D. Coenen
Neurocomputing1
2003 Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors
abstract
Is there a way for an algorithm linked to an unknown body to infer by itself information about this body and the world it is in? Taking the case of space for example, is there a way for this algorithm to realize that its body is in a three dimensional world? Is it possible for this algorithm to discover how to move in a straight line? And more basically: do these questions make any sense at all given that the algorithm only has access to the very high-dimensional data consisting of its sensory inputs and motor outputs? We demonstrate in this article how these questions can be given a positive answer. We show that it is possible to make an algorithm that, by ana- lyzing the law that links its motor outputs to its sensory inputs, discovers information about the structure of the world regardless of the devices constituting the body it is linked to. We present results from simulations demonstrating a way to issue motor orders resulting in “fundamental” movements of the body as regards the structure of the physical world.
David Philipona, J. Kevin O'Regan, Jean-Pierre Nadal, Olivier J. M. D. Coenen
NIPS1
2003 Is There Something Out There? Inferring Space from Sensorimotor Dependencies
abstract
This letter suggests that in biological organisms, the perceived structure of reality, in particular the notions of body, environment, space, object, and attribute, could be a consequence of an effort on the part of brains to account for the dependency between their inputs and their outputs in terms of a small number of parameters. To validate this idea, a procedure is demonstrated whereby the brain of a (simulated) organism with arbitrary input and output connectivity can deduce the dimensionality of the rigid group of the space underlying its input-output relationship, that is, the dimension of what the organism will call physical space.
David Philipona, J. Kevin O'Regan, Jean-Pierre Nadal
Neural Comput.1