Demo Data from SIGIR 2026 / reviewers in the wild / expert
Benjamin P. Hiles
dblp:45/4952
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning paradigms
unsupervised learning |
0.0 | 1 | 2001 | Probabilistic principles in unsupervised learning of visual structure: human data and a model · NIPS 2001 |
Computer vision › 3D vision
visual structure representation |
0.0 | 1 | 2001 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Probabilistic principles in unsupervised learning of visual structure: human data and a modelabstractTo 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 |
NIPS | 2 |