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Jonathan Rubin
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2020 – today
- 2023
- [j8]Nicholas D. Theis, Jonathan Rubin
, Joshua Cape, Satish Iyengar, Konasale M. Prasad
Threshold Selection for Brain Connectomes. Brain Connect. 13(7): 383-393 (2023) - [c28]Jonathan Rubin, Jason Crowley, George Leung, Morteza Ziyadi, Maria Minakova:
Entity Contrastive Learning in a Large-Scale Virtual Assistant System. ACL (industry) 2023: 159-171 - [c27]Li Chen, Jonathan Rubin, Jiahong Ouyang, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W. Gregory, Cynthia Gregory, Meihua Zhu, David O. Kessler, Laurie Malia, Almaz Dessie, Joni Rabiner, Di Coneybeare, Bo Shopsin, Andrew Hersh, Cristian Madar, Jeffrey Shupp, Laura S. Johnson, Jacob Avila, Kristin Dwyer, Peter Weimersheimer, Balasundar Raju, Jochen Kruecker, Alvin Chen:
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos. ISBI 2023: 1-5 - 2021
- [c26]Annamalai Natarajan, Gregory Boverman, Yale Chang, Corneliu Antonescu, Jonathan Rubin:
Convolution-Free Waveform Transformers for Multi-Lead ECG Classification. CinC 2021: 1-4 - [c25]Jonathan Rubin, Ramon Erkamp, Ragha Srinivasa Naidu, Anumod Odungatta Thodiyil, Alvin Chen:
Attention Distillation for Detection Transformers: Application to Real-Time Video Object Detection in Ultrasound. ML4H@NeurIPS 2021: 26-37 - [i13]Asif Rahman, Yale Chang, Jonathan Rubin:
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series. CoRR abs/2109.07602 (2021) - [i12]Annamalai Natarajan, Gregory Boverman, Yale Chang, Corneliu Antonescu, Jonathan Rubin:
Convolution-Free Waveform Transformers for Multi-Lead ECG Classification. CoRR abs/2109.15129 (2021) - 2020
- [c24]Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij, Jonathan Rubin:
A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification. CinC 2020: 1-4 - [c23]Yumin Liu, Claire Zhao, Jonathan Rubin:
Uncertainty Quantification in Chest X-Ray Image Classification using Bayesian Deep Neural Networks. KDH@ECAI 2020: 19-26
2010 – 2019
- 2019
- [c22]Yale Chang, Jonathan Rubin, Gregory Boverman
A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series. CinC 2019: 1-4 - [c21]Jonathan Rubin, Sayed Mazdak Abulnaga
CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation. ICHI 2019: 1-7 - [i11]Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth J. Berkowitz, Sandeep Dalal, William M. Wells III, Steven Horng, Polina Golland:
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images. CoRR abs/1902.10785 (2019) - [i10]Jonathan Rubin, Sayed Mazdak Abulnaga:
CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation. CoRR abs/1904.13281 (2019) - 2018
- [j7]Jonathan Rubin, Cristhian Potes, Minnan Xu-Wilson, Junzi Dong, Asif Rahman, Hiep Nguyen, David Moromisato:
An ensemble boosting model for predicting transfer to the pediatric intensive care unit. Int. J. Medical Informatics 112: 15-20 (2018) - [c20]Saman Parvaneh, Jonathan Rubin:
Electrocardiogram Monitoring and Interpretation: From Traditional Machine Learning to Deep Learning, and Their Combination. CinC 2018: 1-4 - [c19]Saman Parvaneh, Jonathan Rubin, Ali Samadani, Gajendra Katuwal:
Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals. CinC 2018: 1-4 - [c18]Sayed Mazdak Abulnaga
Ischemic Stroke Lesion Segmentation in CT Perfusion Scans Using Pyramid Pooling and Focal Loss. BrainLes@MICCAI (1) 2018: 352-363 - [e1]Kerstin Bach, Razvan C. Bunescu, Oladimeji Farri, Aili Guo, Sadid A. Hasan, Zina M. Ibrahim, Cindy Marling, Jesse Raffa, Jonathan Rubin, Honghan Wu:
Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018), Stockholm, Schweden, July 13, 2018. CEUR Workshop Proceedings 2148, CEUR-WS.org 2018 [contents] - [i9]Jonathan Rubin, Deepan Sanghavi, Claire Zhao, Kathy Lee, Ashequl Qadir, Minnan Xu-Wilson:
Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks. CoRR abs/1804.07839 (2018) - [i8]Saman Parvaneh, Jonathan Rubin, Ali Samadani, Gajendra Katuwal:
Automatic Detection of Arousals during Sleep using Multiple Physiological Signals. CoRR abs/1810.02726 (2018) - [i7]Sayed Mazdak Abulnaga, Jonathan Rubin:
Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss. CoRR abs/1811.01085 (2018) - [i6]Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, Una-May O'Reilly:
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection. CoRR abs/1811.06106 (2018) - 2017
- [j6]David E. Burstein, Jonathan Rubin
Sufficient Conditions for Graphicality of Bidegree Sequences. SIAM J. Discret. Math. 31(1): 50-62 (2017) - [c17]Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh:
Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings. CinC 2017 - [c16]Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan:
Recognizing Abnormal Heart Sounds Using Deep Learning. KDH@IJCAI 2017: 13-19 - [i5]Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan:
Recognizing Abnormal Heart Sounds Using Deep Learning. CoRR abs/1707.04642 (2017) - [i4]Jonathan Rubin, Cristhian Potes, Minnan Xu-Wilson, Junzi Dong, Asif Rahman, Hiep Nguyen, David Moromisato:
An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit. CoRR abs/1707.04958 (2017) - [i3]Jonathan Rubin, Saman Parvaneh, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh:
Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings. CoRR abs/1710.05817 (2017) - 2016
- [j5]Jonathan Rubin, Nachum Ulanovsky
The Representation of Prediction Error in Auditory Cortex. PLoS Comput. Biol. 12(8) (2016) - [c15]Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan:
Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel-Frequency Cepstral Coefficients. CinC 2016 - [c14]Jonathan Rubin, Rui Abreu, Shane Ahern, Hoda Eldardiry, Daniel G. Bobrow:
Time, frequency & complexity analysis for recognizing panic states from physiologic time-series. PervasiveHealth 2016: 81-88 - 2015
- [c13]Jonathan Rubin, Hoda Eldardiry
Towards a mobile and wearable system for predicting panic attacks. UbiComp 2015: 529-533 - [c12]Luis Cruz
A wearable and mobile intervention delivery system for individuals with panic disorder. MUM 2015: 175-182 - [i2]David E. Burstein, Jonathan Rubin:
Sufficient Conditions for Graphicality of Bidegree Sequences. CoRR abs/1511.02411 (2015) - [i1]David E. Burstein, Jonathan Rubin:
Degree switching and partitioning for enumerating graphs to arbitrary orders of accuracy. CoRR abs/1511.03738 (2015) - 2014
- [r3]Jonathan Rubin:
Basal Ganglia: Overview. Encyclopedia of Computational Neuroscience 2014 - [r2]Jonathan Rubin:
Comparative Analysis of Half-Center Central Pattern Generators (CPGs). Encyclopedia of Computational Neuroscience 2014 - [r1]Jonathan Rubin, Cameron C. McIntyre:
Computational Models of Deep Brain Stimulation (DBS). Encyclopedia of Computational Neuroscience 2014 - 2013
- [j4]Nolan Bard, John Alexander Hawkin, Jonathan Rubin, Martin Zinkevich:
The Annual Computer Poker Competition. AI Mag. 34(2): 112- (2013) - [c11]Michael Silva, Silas McCroskey, Jonathan Rubin, Michael Youngblood, Ashwin Ram:
Learning from Demonstration to Be a Good Team Member in a Role Playing Game. FLAIRS 2013 - [c10]Jonathan Rubin, Ian D. Watson:
Decision Generalisation from Game Logs in No Limit Texas Hold'em. IJCAI 2013: 3062-3066 - 2012
- [j3]Jonathan Rubin, Ian D. Watson
Case-based strategies in computer poker. AI Commun. 25(1): 19-48 (2012) - [c9]Jonathan Rubin, Ian D. Watson
Opponent Type Adaptation for Case-Based Strategies in Adversarial Games. ICCBR 2012: 357-368 - [c8]Ian D. Watson
SARTRE: a case-based poker web app. IE 2012: 23 - 2011
- [j2]Jonathan Rubin, Ian D. Watson
Computer poker: A review. Artif. Intell. 175(5-6): 958-987 (2011) - [c7]Jonathan Rubin, Ian D. Watson
Successful Performance via Decision Generalisation in No Limit Texas Hold'em. ICCBR 2011: 467-481 - [c6]Jonathan Rubin, Ian D. Watson
On Combining Decisions from Multiple Expert Imitators for Performance. IJCAI 2011: 344-349 - 2010
- [c5]Jonathan Rubin, Ian D. Watson
Similarity-Based Retrieval and Solution Re-use Policies in the Game of Texas Hold'em. ICCBR 2010: 465-479
2000 – 2009
- 2009
- [j1]Ian D. Watson, Jonathan Rubin:
Playing Texas Hold'em Poker Online Using Case-Based Reasoning. Int. J. Intell. Games Simul. 5(2): 6-13 (2009) - [c4]Jonathan Rubin, Ian D. Watson
A Memory-Based Approach to Two-Player Texas Hold'em. Australasian Conference on Artificial Intelligence 2009: 465-474 - 2008
- [c3]Ian D. Watson
CASPER: A Case-Based Poker-Bot. Australasian Conference on Artificial Intelligence 2008: 594-600 - [c2]Ian D. Watson
Improving a case-based texas hold'em poker bot. CIG 2008: 350-356 - 2007
- [c1]Jonathan Rubin, Ian D. Watson:
Investigating the Effectiveness of Applying Case-Based Reasoning to the Game of Texas Hold'em. FLAIRS 2007: 417-422
Coauthor Index
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