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Archisman Rudra

dblp:18/3568 · DBLP profile ↗
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2ranked-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 · 2

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
2 papers
Knowledge representation and reasoning · 39% Probabilistic and Bayesian machine learning · 39% Learning theory · 22%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
markov chain
0.012001
Fragment Completion in Humans and Machines · NIPS 2001
Knowledge, reasoning and agents › Knowledge representation and reasoning › cognitive modeling › cognitive architecture
memory modeling
0.012001
Fragment Completion in Humans and Machines · NIPS 2001
Machine learning › Learning theory › statistical estimation
sufficient statistics
0.011997
Features as Sufficient Statistics · NIPS 1997

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

markov model · 0.1bottom-up and top-down computation · 0.1sufficient statistics · 0.0
YearPublicationVenuePosition
2001 Fragment Completion in Humans and Machines
abstract
Partial information can trigger a complete memory. At the same time, human memory is not perfect. A cue can contain enough information to specify an item in memory, but fail to trigger that item. In the context of word memory, we present experiments that demonstrate some basic patterns in human memory errors. We use cues that consist of word frag- ments. We show that short and long cues are completed more accurately than medium length ones and study some of the factors that lead to this behavior. We then present a novel computational model that shows some of the flexibility and patterns of errors that occur in human memory. This model iterates between bottom-up and top-down computations. These are tied together using a Markov model of words that allows memory to be accessed with a simple feature set, and enables a bottom-up process to compute a probability distribution of possible completions of word frag- ments, in a manner similar to models of visual perceptual completion.
David Jacobs 0001, Bas Rokers, Archisman Rudra
NIPS3
1997 Features as Sufficient Statistics
Davi Geiger, Archisman Rudra, Laurance T. Maloney
NIPS2