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2020 – today
- 2024
[c25]Geoffrey Challen
, Victoria Dean, Nate Derbinsky, Matt X. Wang, Jacqueline Smith:
Interviewing the Teaching Faculty Hiring Process. SIGCSE (2) 2024: 1525-1526- 2022
- [c24]Ying Zhao, Erik Hemberg, Nate Derbinsky, Gabino Mata, Una-May O'Reilly:
Using domain knowledge in coevolution and reinforcement learning to simulate a logistics enterprise. GECCO Companion 2022: 514-517 - 2021
- [c23]Ying Zhao, Erik Hemberg, Nate Derbinsky, Gabino Mata, Una-May O'Reilly:
Simulating a logistics enterprise using an asymmetrical wargame simulation with soar reinforcement learning and coevolutionary algorithms. GECCO Companion 2021: 1907-1915
2010 – 2019
- 2019
- [c22]Todd W. Neller, Raja Sooriamurthi, Michael Guerzhoy, Lisa Zhang, Paul Talaga, Christopher Archibald, Adam Summerville, Joseph C. Osborn, Cinjon Resnick, Avital Oliver, Surya Bhupatiraju, Kumar Krishna Agrawal, Nate Derbinsky, Elena Strange, Marion Neumann, Jonathan Chen, Zac Christensen, Michael Wollowski, Oscar Youngquist:
Model AI Assignments 2019. AAAI 2019: 9751-9753 - 2018
- [c21]Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth:
Model AI Assignments 2018. AAAI 2018: 7959-7961 - 2017
- [c20]Nate Derbinsky, Tyler M. Frasca:
Cornhole: A Widely-Accessible AI Robotics Task. AAAI 2017: 4771-4774 - [c19]Ying Zhao, Emily Mooren, Nate Derbinsky:
Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning. KEOD 2017: 233-238 - [c18]Nate Derbinsky, Durga Suresh:
Sustainable Methods for Impactful Service Learning in Computer Science (Abstract Only). SIGCSE 2017: 723 - 2016
- [c17]Todd W. Neller, Laura E. Brown, James B. Marshall, Lisa Torrey, Nate Derbinsky, Andrew A. Ward, Thomas E. Allen, Judy Goldsmith, Nahom Muluneh:
Model AI Assignments 2016. AAAI 2016: 4139-4141 - [c16]Tyler M. Frasca, Antonio G. Sestito, Craig Versek, Douglas E. Dow, Barry C. Husowitz, Nate Derbinsky:
A Comparison of Supervised Learning Algorithms for Telerobotic Control Using Electromyography Signals. AAAI 2016: 4208-4211 - [c15]Ning Hao, AmirReza Oghbaee, Mohammad Rostami, Nate Derbinsky, José Bento:
Testing Fine-Grained Parallelism for the ADMM on a Factor-Graph. IPDPS Workshops 2016: 835-844 - [i6]Ning Hao, AmirReza Oghbaee, Mohammad Rostami, Nate Derbinsky, José Bento:
Testing fine-grained parallelism for the ADMM on a factor-graph. CoRR abs/1603.02526 (2016) - 2015
- [c14]Charles Mathy, Nate Derbinsky, José Bento, Jonathan Rosenthal, Jonathan S. Yedidia:
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning. AAAI 2015: 2864-2870 - [c13]José Bento, Nate Derbinsky, Charles Mathy, Jonathan S. Yedidia:
Proximal Operators for Multi-Agent Path Planning. AAAI 2015: 3657-3663 - [i5]José Bento, Nate Derbinsky, Charles Mathy, Jonathan S. Yedidia:
Proximal operators for multi-agent path planning. CoRR abs/1504.01783 (2015) - [i4]Charles Mathy, Nate Derbinsky, José Bento, Jonathan Rosenthal, Jonathan S. Yedidia:
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning. CoRR abs/1505.02867 (2015) - 2013
- [j1]Nate Derbinsky, John E. Laird:
Effective and efficient forgetting of learned knowledge in Soar's working and procedural memories. Cogn. Syst. Res. 24: 104-113 (2013) - [c12]Nate Derbinsky, José Bento, Jonathan S. Yedidia:
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing. AAAI Fall Symposia 2013 - [c11]José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia:
A message-passing algorithm for multi-agent trajectory planning. NIPS 2013: 521-529 - [i3]Nate Derbinsky, José Bento, Veit Elser, Jonathan S. Yedidia:
An Improved Three-Weight Message-Passing Algorithm. CoRR abs/1305.1961 (2013) - [i2]Nate Derbinsky, José Bento, Jonathan S. Yedidia:
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing. CoRR abs/1311.4064 (2013) - [i1]José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia:
A message-passing algorithm for multi-agent trajectory planning. CoRR abs/1311.4527 (2013) - 2012
- [b1]Nate Derbinsky:
Effective and Efficient Memory for Generally Intelligent Agents. University of Michigan, USA, 2012 - [c10]Nate Derbinsky, Justin Li, John E. Laird:
A Multi-Domain Evaluation of Scaling in a General Episodic Memory. AAAI 2012: 193-199 - [c9]Justin Li, Nate Derbinsky, John E. Laird:
Functional Interactions Between Memory and Recognition Judgments. AAAI 2012: 228-234 - [c8]Nate Derbinsky, Justin Li, John E. Laird:
Algorithms for scaling in a general episodic memory. AAMAS 2012: 1387-1388 - [c7]Nate Derbinsky, Georg Essl:
Exploring Reinforcement Learning for Mobile Percussive Collaboration. NIME 2012 - 2011
- [c6]Nate Derbinsky:
Long-Term Declarative Memory for Generally Intelligent Agents. AAAI 2011 - [c5]Nate Derbinsky, John E. Laird:
A Functional Analysis of Historical Memory Retrieval Bias in the Word Sense Disambiguation Task. AAAI 2011: 663-668 - [c4]Nate Derbinsky, John E. Laird:
Effective and Efficient Management of Soar's Working Memory via Base-Level Activation. AAAI Fall Symposium: Advances in Cognitive Systems 2011 - [c3]John Edwin Laird, Nate Derbinsky, Miller Tinkerhess:
A Case Study in Integrating Probabilistic Decision Making and Learning in a Symbolic Cognitive Architecture: Soar Plays Dice. AAAI Fall Symposium: Advances in Cognitive Systems 2011 - [c2]Nate Derbinsky, Georg Essl:
Cognitive Architecture in Mobile Music Interactions. NIME 2011: 104-107
2000 – 2009
- 2009
- [c1]Nate Derbinsky, John E. Laird:
Efficiently Implementing Episodic Memory. ICCBR 2009: 403-417
Coauthor Index
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