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Eric Weiss

dblp:178/9389 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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
3D vision · 50% Deep learning architectures and training · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 44% Processor architecture and microarchitecture · 44% Performance modeling and evaluation · 13%

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

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › attention mechanism
attention learning		 0.312017
Emergence of foveal image sampling from learning to attend in visual scenes · ICLR (Poster) 2017
Processor architecture and microarchitecture › special-purpose processor
digital differential analyzer		 0.011952
Applications of CRC-105 decimal digital differential analyzer · Trans. I R E Prof. Group Electron. Comput. 1952
Electronic design automation
logic synthesis		 0.011952
Applications of CRC-105 decimal digital differential analyzer · Trans. I R E Prof. Group Electron. Comput. 1952
Performance modeling and evaluation
simulation		 0.011952
Applications of CRC-105 decimal digital differential analyzer · Trans. I R E Prof. Group Electron. Comput. 1952

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

reinforcement learning · 0.3attention mechanism · 0.3
YearPublicationVenuePosition
2017 Emergence of foveal image sampling from learning to attend in visual scenes
Brian Cheung, Eric Weiss, Bruno A. Olshausen
 ICLR (Poster)2
1952 Applications of CRC-105 decimal digital differential analyzer
abstract
In the two years since the first digital differential analyzer was put in operation, it has been the complexity of filling and operating such machines, rather than any difficulty in coding them for a problem, that has constituted the main impediment to convenience and efficiency in their use. In filling some hundred-odd binary digits, one at a time, for each integrator information, the possibility of error is great. For instance, let us look at the binary number
Eric Weiss
 Trans. I R E Prof. Group Electron. Comput.1