J. Alspector

1.1k citations
45 papers · 734 indexed · h-index 16

J. Alspector

43 papers receiving 665 citations

Peers

J. Alspector
Comparison fields: 5 of 74
  • Nuclear and High Energy Physics 174
  • Artificial Intelligence 342
  • Computer Vision and Pattern Recognition 140
  • Statistical and Nonlinear Physics 67
  • Information Systems 118
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Marco Lanzagorta United States
Steven Rosenberg United States
Vasile Manta Romania
J. Duarte United States
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Citations per year

Countries citing papers authored by J. Alspector

Since Specialization
Citations

This map shows the geographic impact of J. Alspector's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by J. Alspector with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Alspector more than expected).

Fields of papers citing papers by J. Alspector

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Alspector. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by J. Alspector. The network helps show where J. Alspector may publish in the future.

Co-authorship network

The 25 scholars most cited alongside J. Alspector, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with J. Alspector Line = papers co-authored together J. Alspector links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20201
2
The Impact of Feature Selection on Signature-Driven Spam Detection.
200424
3 19991
4 199730
5
A Study of Parallel Perturbative Gradient Descent
19948
6
A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks
199268
7 19924
8
Experimental Evaluation of Learning in a Neural Microsystem
199122
9
A VLSI-efficient technique for generating multiple uncorrelated noise sources and its application to stochastic neural networks
19910
10
Relaxation Networks for Large Supervised Learning Problems
199024
11 19908
12
Performance of a Stochastic Learning Microchip
198823
13
Stochastic Learning Networks and their Electronic Implementation
198727
14 198113
15 19796
16 197913
17 19799
18 197332
19 19723
20 19679

About J. Alspector

J. Alspector is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing and Cognitive Neuroscience, having authored 45 papers that have together received 734 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (14 papers), Neural Networks and Applications (14 papers), Quantum Chromodynamics and Particle Interactions (12 papers), High-Energy Particle Collisions Research (11 papers), Advanced Memory and Neural Computing (9 papers), Neural dynamics and brain function (4 papers), Analog and Mixed-Signal Circuit Design (3 papers) and Model Reduction and Neural Networks (3 papers). The work is most often cited by research in Nuclear and High Energy Physics (174 citations), Artificial Intelligence (342 citations), Computer Vision and Pattern Recognition (140 citations), Statistical and Nonlinear Physics (67 citations) and Information Systems (118 citations). J. Alspector has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Aleksander Kołcz, Abdur Chowdhury, Robert B. Allen, N. Karunanithi, G. Kalbfleisch, A. Jayakumar, J.W. Gannett, George V. Popescu, Robert Carlson and Marijke F. Augusteijn. Their work appears in journals such as Physics Letters B, Physical Review Letters, Information Retrieval, Pattern Analysis and Applications and Nuclear Physics B.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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