Irina Rish

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26 artikel

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Guillermo CecchiIBM ResearchEmail yang diverifikasi di us.ibm.com
Pouya BashivanMcGill university, MilaEmail yang diverifikasi di mcgill.ca
Dimitri KanevskyRessearcher, IBM T.J. WatsonEmail yang diverifikasi di us.ibm.com
Ricardo VilaltaUniversity of AustinEmail yang diverifikasi di ieee.org
Katya ScheinbergGeorgia TechEmail yang diverifikasi di gatech.edu
Mohammed YeasinUniversity of MemphisEmail yang diverifikasi di memphis.edu
Noel CodellaPrincipal Researcher @ MicrosoftEmail yang diverifikasi di microsoft.com
Bhuvana RamabhadranManager, GoogleEmail yang diverifikasi di google.com
Joseph HellersteinUniversity of WashingtonEmail yang diverifikasi di uw.edu
Rahul GargProfessor, Computer Science and Engineering, Indian Institute of Technology, DelhiEmail yang diverifikasi di cse.iitd.ac.in
Dan HeIBM T. J. WatsonEmail yang diverifikasi di us.ibm.com
Marwan N BalikiNorthwestern UniversityEmail yang diverifikasi di northwestern.edu
Apkar Vania ApkarianNorthwestern UniversityEmail yang diverifikasi di northwestern.edu
JB PolineMNI, McGill University and UC BerkeleyEmail yang diverifikasi di mcgill.ca
Marion PlazePsychiatrist at Sainte-Anne hospital, Paris Descartes University & INSERM U894Email yang diverifikasi di ch-sainte-anne.fr
Bertrand ThirionInriaEmail yang diverifikasi di inria.fr
Aurelie LozanoSenior Research Scientist, IBM ResearchEmail yang diverifikasi di us.ibm.com
T.S. JayramLawrence Livermore National LaboratoryEmail yang diverifikasi di llnl.gov
Aleks JakulinKatalysEmail yang diverifikasi di stat.columbia.edu
Francisco PereiraMachine Learning Core, National Institute of Mental HealthEmail yang diverifikasi di nih.gov

Ikuti

Irina Rish

University of Montreal / Mila -Quebec AI Institute

Email yang diverifikasi di mila.quebec - Beranda

Artificial Intelligence Machine Learning Neuroscience


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
An empirical study of the naive Bayes classifier I Rish IJCAI 2001 workshop on empirical methods in artificial intelligence 3 (22 …, 2001	5991	2001
Learning representations from EEG with deep recurrent-convolutional neural networks P Bashivan, I Rish, M Yeasin, N Codella arXiv preprint arXiv:1511.06448, 2015	928	2015
Learning to learn without forgetting by maximizing transfer and minimizing interference M Riemer, I Cases, R Ajemian, M Liu, I Rish, Y Tu, G Tesauro arXiv preprint arXiv:1810.11910, 2018	927	2018
Critical event prediction for proactive management in large-scale computer clusters RK Sahoo, AJ Oliner, I Rish, M Gupta, JE Moreira, S Ma, R Vilalta, ... Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003	384	2003
Towards continual reinforcement learning: A review and perspectives K Khetarpal, M Riemer, I Rish, D Precup Journal of Artificial Intelligence Research 75, 1401-1476, 2022	359	2022
Mini-buckets: A general scheme for bounded inference R Dechter, I Rish Journal of the ACM (JACM) 50 (2), 107-153, 2003	303	2003
Invariance principle meets information bottleneck for out-of-distribution generalization K Ahuja, E Caballero, D Zhang, JC Gagnon-Audet, Y Bengio, I Mitliagkas, ... Advances in Neural Information Processing Systems 34, 3438-3450, 2021	294	2021
Sparse modeling: theory, algorithms, and applications I Rish, G Grabarnik CRC press, 2014	282	2014
Improving network robustness by edge modification A Beygelzimer, G Grinstein, R Linsker, I Rish Physica A: Statistical Mechanics and its Applications 357 (3-4), 593-612, 2005	274	2005
Adaptive diagnosis in distributed systems I Rish, M Brodie, S Ma, N Odintsova, A Beygelzimer, G Grabarnik, ... IEEE Transactions on neural networks 16 (5), 1088-1109, 2005	247	2005
Survey on applications of multi-armed and contextual bandits D Bouneffouf, I Rish, C Aggarwal 2020 IEEE congress on evolutionary computation (CEC), 1-8, 2020	244	2020
Directional resolution: The davis-putnam procedure, revisited R Dechter, I Rish Principles of knowledge representation and reasoning, 134-145, 1994	230	1994
Prediction and interpretation of distributed neural activity with sparse models MK Carroll, GA Cecchi, I Rish, R Garg, AR Rao NeuroImage 44 (1), 112-122, 2009	224	2009
A survey on practical applications of multi-armed and contextual bandits D Bouneffouf, I Rish arXiv preprint arXiv:1904.10040, 2019	194	2019
Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, ... Advances in Neural Information Processing Systems 33, 16532-16545, 2020	172*	2020
Resolution versus search: Two strategies for SAT I Rish, R Dechter Journal of Automated Reasoning 24 (1), 225-275, 2000	167	2000
Active sampling collaborative prediction method for end-to-end performance prediction I Rish, GJ Tesauro US Patent 7,640,224, 2009	164	2009
Using language processing and speech analysis for the identification of psychosis and other disorders CM Corcoran, GA Cecchi Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 5 (8), 770-779, 2020	157	2020
An analysis of data characteristics that affect naive Bayes performance I Rish, J Hellerstein, J Thathachar IBM TJ Watson Research Center 30, 1-8, 2001	155	2001
Real-time problem determination in distributed systems using active probing I Rish, M Brodie, N Odintsova, S Ma, G Grabarnik 2004 IEEE/IFIP Network Operations and Management Symposium (IEEE Cat. No …, 2004	143	2004

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