Craig M. Vineyard

847 total citations
35 papers, 295 citations indexed

About

Craig M. Vineyard is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Cognitive Neuroscience. According to data from OpenAlex, Craig M. Vineyard has authored 35 papers receiving a total of 295 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 22 papers in Electrical and Electronic Engineering and 13 papers in Cognitive Neuroscience. Recurrent topics in Craig M. Vineyard's work include Advanced Memory and Neural Computing (20 papers), Neural Networks and Reservoir Computing (13 papers) and Neural dynamics and brain function (11 papers). Craig M. Vineyard is often cited by papers focused on Advanced Memory and Neural Computing (20 papers), Neural Networks and Reservoir Computing (13 papers) and Neural dynamics and brain function (11 papers). Craig M. Vineyard collaborates with scholars based in United States. Craig M. Vineyard's co-authors include James B. Aimone, William Severa, Stephen Verzi, Conrad D. James, Nadine E. Miner, Kristofor D. Carlson, Timothy J. Draelos, Steven J. Plimpton, Aleksandra Faust and Matthew Marinella and has published in prestigious journals such as Optics Express, Computer and Neural Computation.

In The Last Decade

Craig M. Vineyard

33 papers receiving 282 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Craig M. Vineyard United States 7 193 157 95 45 31 35 295
Shasha Guo China 9 196 1.0× 93 0.6× 74 0.8× 49 1.1× 43 1.4× 24 267
William Severa United States 8 177 0.9× 148 0.9× 74 0.8× 40 0.9× 23 0.7× 31 273
Gregor Lenz France 5 274 1.4× 126 0.8× 134 1.4× 73 1.6× 18 0.6× 12 361
Jianhao Ding China 6 325 1.7× 128 0.8× 220 2.3× 62 1.4× 25 0.8× 19 388
Man Yao China 7 274 1.4× 127 0.8× 175 1.8× 37 0.8× 41 1.3× 15 366
Adarsh Kumar Kosta United States 8 167 0.9× 79 0.5× 69 0.7× 21 0.5× 24 0.8× 18 222
Amirreza Yousefzadeh Netherlands 13 356 1.8× 114 0.7× 136 1.4× 118 2.6× 30 1.0× 37 396
Dingheng Wang China 9 136 0.7× 116 0.7× 91 1.0× 17 0.4× 88 2.8× 15 304
Gourav Datta United States 9 168 0.9× 67 0.4× 71 0.7× 28 0.6× 37 1.2× 25 233
Amar Shrestha United States 10 233 1.2× 97 0.6× 120 1.3× 67 1.5× 13 0.4× 16 271

Countries citing papers authored by Craig M. Vineyard

Since Specialization
Citations

This map shows the geographic impact of Craig M. Vineyard'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 Craig M. Vineyard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Craig M. Vineyard more than expected).

Fields of papers citing papers by Craig M. Vineyard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Craig M. Vineyard. 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 Craig M. Vineyard. The network helps show where Craig M. Vineyard may publish in the future.

Co-authorship network of co-authors of Craig M. Vineyard

This figure shows the co-authorship network connecting the top 25 collaborators of Craig M. Vineyard. A scholar is included among the top collaborators of Craig M. Vineyard based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Craig M. Vineyard. Craig M. Vineyard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Iyer, Prasad P., Gaurang R. Bhatt, Saaketh Desai, et al.. (2025). Is Computing with Light All You Need? A Perspective on Codesign for Optical Artificial Intelligence and Scientific Computing. Advanced Intelligent Systems. 8(1).
2.
Léonard, François, Elliot J. Fuller, Corinne Teeter, & Craig M. Vineyard. (2024). Role of depth in optical diffractive neural networks. Optics Express. 32(13). 23125–23125. 1 indexed citations
3.
Severa, William, et al.. (2023). Neuromorphic Population Evaluation using the Fugu Framework. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–7.
4.
Teeter, Corinne, et al.. (2023). Context Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines. 1–9. 1 indexed citations
5.
Dick, Robert P., Rob Aitken, John Paul Strachan, et al.. (2023). Research Challenges for Energy-Efficient Computing in Automated Vehicles. Computer. 56(3). 47–58. 3 indexed citations
6.
Severa, William, et al.. (2022). Exploring SAR ATR with neural networks: going beyond accuracy. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 14–14. 2 indexed citations
7.
Aimone, James B., et al.. (2021). Spiking Neural Streaming Binary Arithmetic. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 79–83. 3 indexed citations
8.
Chance, Frances S., et al.. (2020). Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence. Frontiers in Computational Neuroscience. 14. 39–39. 10 indexed citations
9.
Vineyard, Craig M., et al.. (2020). RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search. 1–7. 3 indexed citations
10.
Sanyal, Sourav, Aayush Ankit, Craig M. Vineyard, & Kaushik Roy. (2020). Energy-Efficient Target Recognition using ReRAM Crossbars for Enabling On-Device Intelligence. 9843. 1–6. 2 indexed citations
11.
Vineyard, Craig M., et al.. (2020). Comparing Neural Accelerators & Neuromorphic Architectures The False Idol of Operations. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–6. 1 indexed citations
12.
Severa, William, et al.. (2019). Training deep neural networks for binary communication with the Whetstone method. Nature Machine Intelligence. 1(2). 86–94. 69 indexed citations
13.
Vineyard, Craig M., et al.. (2019). Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–7. 5 indexed citations
14.
Vineyard, Craig M., et al.. (2018). Impacts of Mathematical Optimizations on Reinforcement Learning Policy Performance. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 518. 1–8. 1 indexed citations
15.
Vineyard, Craig M., Stephen Verzi, Conrad D. James, James B. Aimone, & Gregory L. Heileman. (2015). Repeated play of the SVM game as a means of adaptive classification. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–8. 2 indexed citations
16.
Vineyard, Craig M., Stephen Verzi, Conrad D. James, James B. Aimone, & Gregory L. Heileman. (2015). MapReduce SVM Game. Procedia Computer Science. 53. 298–307. 1 indexed citations
17.
Vineyard, Craig M., et al.. (2012). A multi-modal network architecture for knowledge discovery. 1(1). 2 indexed citations
18.
Vineyard, Craig M., et al.. (2012). Game theoretic mechanism design applied to machine learning classification. 12. 1–5. 4 indexed citations
19.
Vineyard, Craig M., M. J. R. Healy, Thomas P. Caudell, et al.. (2009). Memory in Silico: Building a Neuromimetic Episodic Cognitive Model. 733–737. 7 indexed citations
20.
Bernard, Michael L., Stephen Verzi, James D. Morrow, et al.. (2009). Temporal semantics: An Adaptive Resonance Theory approach. 1. 3111–3117. 2 indexed citations

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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