Byron Wallace

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Iain MarshallKing's College LondonDirección de correo verificada de kcl.ac.uk
Thomas TrikalinosHealth Services, Policy & Practice and BiostatisticsDirección de correo verificada de brown.edu
Joseph LauBrown UniversityDirección de correo verificada de brown.edu
Sarthak JainML Scientist, ProfluentDirección de correo verificada de profluent.bio
Christopher H SchmidBrown UniversityDirección de correo verificada de brown.edu
Carla BrodleyNortheastern UniversityDirección de correo verificada de neu.edu
Matt LeaseProfessor, School of Information, University of Texas at AustinDirección de correo verificada de utexas.edu
Ani NenkovaPrincipal Scientist at Adobe ResearchDirección de correo verificada de adobe.com
Jay DeYoungAllen Institute For Artificial IntelligenceDirección de correo verificada de allenai.org
Silvio AmirNortheastern UniversityDirección de correo verificada de northeastern.edu
Issa DahabrehHarvard T.H. Chan School of Public HealthDirección de correo verificada de mail.harvard.edu
Kevin SmallPrincipal Applied Scientist, AmazonDirección de correo verificada de amazon.com
James ThomasProfessor of Social Research & Policy, EPPI-Centre, UCL Social Research Institute, UCLDirección de correo verificada de ioe.ac.uk
Junyi Jessy LiAssociate Professor, The University of Texas at AustinDirección de correo verificada de austin.utexas.edu
Eric LehmanMITDirección de correo verificada de mit.edu
Benjamin E NyeColorado CollegeDirección de correo verificada de coloradocollege.edu
Jan-Willem van de MeentUniversity of Amsterdam and Northeastern UniversityDirección de correo verificada de uva.nl
Jered McInerneyNortheastern University, CodaMetrixDirección de correo verificada de northeastern.edu
Somin WadhwaNortheastern UniversityDirección de correo verificada de northeastern.edu
Yinfei YangAppleDirección de correo verificada de apple.com

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Byron Wallace

Associate Professor, Northeastern University

Dirección de correo verificada de ccs.neu.edu - Página principal

natural language processing machine learning machine learning for health model interpretability


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification Y Zhang, B Wallace arXiv preprint arXiv:1510.03820, 2015	20461	2015
Attention is Not Explanation S Jain, B Wallace arXiv preprint arXiv:1902.00264, 2019	1670	2019
Closing the gap between methodologists and end-users: R as a computational back-end BC Wallace, IJ Dahabreh, TA Trikalinos, J Lau, P Trow, CH Schmid Journal of statistical software 49, 1-15, 2012	1158	2012
ERASER: A benchmark to evaluate rationalized NLP models J DeYoung, S Jain, NF Rajani, E Lehman, C Xiong, R Socher, ... arXiv preprint arXiv:1911.03429, 2019	701	2019
Deploying an interactive machine learning system in an evidence-based practice center: abstrackr BC Wallace, K Small, CE Brodley, J Lau, TA Trikalinos Proceedings of the 2nd ACM SIGHIT symposium on International health …, 2012	690	2012
Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data BC Wallace, CH Schmid, J Lau, TA Trikalinos BMC medical research methodology 9 (1), 80, 2009	683	2009
Living systematic review: 1. Introduction—the why, what, when, and how JH Elliott, A Synnot, T Turner, M Simmonds, EA Akl, S McDonald, ... Journal of clinical epidemiology 91, 23-30, 2017	623	2017
Toward systematic review automation: a practical guide to using machine learning tools in research synthesis IJ Marshall, BC Wallace Systematic reviews 8, 1-10, 2019	487	2019
Semi-automated screening of biomedical citations for systematic reviews BC Wallace, TA Trikalinos, J Lau, C Brodley, CH Schmid BMC bioinformatics 11 (1), 55, 2010	405	2010
OpenMEE: Intuitive, open‐source software for meta‐analysis in ecology and evolutionary biology BC Wallace, MJ Lajeunesse, G Dietz, IJ Dahabreh, TA Trikalinos, ... Methods in Ecology and Evolution 8 (8), 941-947, 2017	396	2017
Machine learning for identifying randomized controlled trials: an evaluation and practitioner's guide IJ Marshall, A Noel‐Storr, J Kuiper, J Thomas, BC Wallace Research synthesis methods 9 (4), 602-614, 2018	334	2018
Living systematic reviews: 2. Combining human and machine effort J Thomas, A Noel-Storr, I Marshall, B Wallace, S McDonald, ... Journal of clinical epidemiology 91, 31-37, 2017	334	2017
Modelling context with user embeddings for sarcasm detection in social media S Amir, BC Wallace, H Lyu, PCMJ Silva arXiv preprint arXiv:1607.00976, 2016	322	2016
RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials IJ Marshall, J Kuiper, BC Wallace Journal of the American Medical Informatics Association, 2015	293	2015
Living systematic reviews: 4. Living guideline recommendations EA Akl, JJ Meerpohl, J Elliott, LA Kahale, HJ Schünemann, T Agoritsas, ... Journal of clinical epidemiology 91, 47-53, 2017	281	2017
Class Imbalance, Redux BC Wallace, K Small, CE Brodley, TA Trikalinos Data Mining (ICDM), 2011 IEEE 11th International Conference on, 754-763, 2011	272	2011
A corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature B Nye, JJ Li, R Patel, Y Yang, IJ Marshall, A Nenkova, BC Wallace Proceedings of the conference. Association for Computational Linguistics …, 2018	271	2018
Neural information retrieval: At the end of the early years KD Onal, Y Zhang, IS Altingovde, MM Rahman, P Karagoz, A Braylan, ... Information Retrieval Journal 21, 111-182, 2018	227*	2018
Revisiting relation extraction in the era of large language models S Wadhwa, S Amir, BC Wallace Proceedings of the conference. Association for Computational Linguistics …, 2023	215	2023
Active Discriminative Text Representation Learning. Y Zhang, M Lease, BC Wallace AAAI, 3386-3392, 2017	198	2017

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