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Benjamin P. Hiles

dblp:45/4952 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

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

Artificial intelligence and machine learning · 1

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
3D vision · 30% Learning paradigms · 30% Probabilistic and Bayesian machine learning · 30%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning paradigms
unsupervised learning
0.012001
Probabilistic principles in unsupervised learning of visual structure: human data and a model · NIPS 2001
Computer vision › 3D vision
visual structure representation
0.012001
Probabilistic principles in unsupervised learning of visual structure: human data and a model · NIPS 2001

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

probabilistic model · 0.0human subject experiment · 0.0
YearPublicationVenuePosition
2001 Probabilistic principles in unsupervised learning of visual structure: human data and a model
abstract
To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images with tight control exerted over (1) the condi- tional probabilities of the constituent fragments, and (2) the value of Bar- low’s criterion of “suspicious coincidence” (the ratio of joint probability to the product of marginals). We then compared the part verification re- sponse times for various probe/target combinations before and after the exposure. For composite probes, the speedup was much larger for tar- gets that contained pairs of fragments perfectly predictive of each other, compared to those that did not. This effect was modulated by the sig- nificance of their co-occurrence as estimated by Barlow’s criterion. For lone-fragment probes, the speedup in all conditions was generally lower than for composites. These results shed light on the brain’s strategies for unsupervised acquisition of structural information in vision.
Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Nathan Intrator
NIPS2