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Wayne Imaino

dblp:15/4076 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Emerging computing paradigms · 100%
Artificial intelligence
1 paper
Representation and self-supervised learning · 77% Deep learning architectures and training · 23%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › representation learning › unsupervised representation learning
sparse coding		 0.212015
Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory · AAAI 2015
Emerging computing paradigms › neuromorphic computing
hierarchical temporal memory		 0.212015
Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory · AAAI 2015
Emerging computing paradigms
neuromorphic computing		 0.212015
Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory · AAAI 2015
Machine learning › Deep learning architectures and training
weight initialization		 0.112015
Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory · AAAI 2015

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

spatial pooler · 0.4
YearPublicationVenuePosition
2015 Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory
abstract
In the Hierarchical Temporal Memory (HTM) paradigm the effect of overlap between inputs on the activation of columns in the spatial pooler is studied. Numerical results suggest that similar inputs are represented by similar sets of columns and dissimilar inputs are represented by dissimilar sets of columns. It is shown that the spatial pooler produces these results under certain conditions for the connectivity and proximal thresholds at initialization. Qualitative arguments about the learning dynamics of the spatial pooler are then discussed.
Mackenzie Leake, Liyu Xia, Kamil Rocki, Wayne Imaino
 AAAI4