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Daniel L. James

dblp:43/1325 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 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
Deep learning architectures and training · 67% Representation and self-supervised learning · 33%

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

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › biologically inspired neural network
self-organizing neural network		 0.011994
SARDNET: A Self-Organizing Feature Map for Sequences · NIPS 1994
Machine learning › Deep learning architectures and training › sequence modeling
sequence classification		 0.011994
SARDNET: A Self-Organizing Feature Map for Sequences · NIPS 1994
Machine learning › Representation and self-supervised learning › representation learning
sequence representation		 0.011994
SARDNET: A Self-Organizing Feature Map for Sequences · NIPS 1994

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

kohonen feature map · 0.0activation retention and decay · 0.0
YearPublicationVenuePosition
1994 SARDNET: A Self-Organizing Feature Map for Sequences
abstract
A self-organizing neural network for sequence classification called SARDNET is described and analyzed experimentally. SARDNET extends the Kohonen Feature Map architecture with activation re(cid:173) tention and decay in order to create unique distributed response patterns for different sequences. SARDNET yields extremely dense yet descriptive representations of sequential input in very few train(cid:173) ing iterations. The network has proven successful on mapping ar(cid:173) bitrary sequences of binary and real numbers, as well as phonemic representations of English words. Potential applications include isolated spoken word recognition and cognitive science models of sequence processing.
Daniel L. James, Risto Miikkulainen
 NIPS1