Jennifer Dy
Jennifer Dy
Electrical and Computer Engineering, Northeastern University
Verified email at
Cited by
Cited by
Feature selection for unsupervised learning
JG Dy, CE Brodley
Journal of machine learning research 5 (Aug), 845-889, 2004
Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors
S Patel, K Lorincz, R Hughes, N Huggins, J Growdon, D Standaert, ...
IEEE transactions on information technology in biomedicine 13 (6), 864-873, 2009
Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks
JM Brown, JP Campbell, A Beers, K Chang, S Ostmo, RVP Chan, J Dy, ...
JAMA ophthalmology 136 (7), 803-810, 2018
Learning to prompt for continual learning
Z Wang, Z Zhang, CY Lee, H Zhang, R Sun, X Ren, G Su, V Perot, J Dy, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
Impact of imputation of missing values on classification error for discrete data
A Farhangfar, L Kurgan, J Dy
Pattern Recognition 41 (12), 3692-3705, 2008
Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories.
EH Siegel, MK Sands, W Van den Noortgate, P Condon, Y Chang, J Dy, ...
Psychological bulletin 144 (4), 343, 2018
Active learning from crowds
Y Yan, GM Fung, R Rosales, JG Dy
Proceedings of the 28th international conference on machine learning (ICML …, 2011
Feature subset selection and order identification for unsupervised learning
JG Dy, CE Brodley
Icml, 247-254, 2000
Unsupervised feature selection applied to content-based retrieval of lung images
JG Dy, CE Brodley, A Kak, LS Broderick, AM Aisen
IEEE transactions on pattern analysis and machine intelligence 25 (3), 373-378, 2003
Cluster: An unsupervised algorithm for modeling Gaussian mixtures
CA Bouman, M Shapiro, GW Cook, CB Atkins, H Cheng
Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations
P Sakornsakolpat, D Prokopenko, M Lamontagne, NF Reeve, AL Guyatt, ...
Nature genetics 51 (3), 494-505, 2019
Dualprompt: Complementary prompting for rehearsal-free continual learning
Z Wang, Z Zhang, S Ebrahimi, R Sun, H Zhang, CY Lee, X Ren, G Su, ...
European Conference on Computer Vision, 631-648, 2022
Evolving feature selection
H Liu, ER Dougherty, JG Dy, K Torkkola, E Tuv, H Peng, C Ding, F Long, ...
IEEE Intelligent systems 20 (6), 64-76, 2005
Modeling annotator expertise: Learning when everybody knows a bit of something
Y Yan, R Rosales, G Fung, M Schmidt, G Hermosillo, L Bogoni, L Moy, ...
Proceedings of the thirteenth international conference on artificial …, 2010
Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment
AM Aisen, LS Broderick, H Winer-Muram, CE Brodley, AC Kak, ...
Radiology 228 (1), 265-270, 2003
Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting
A Al-Shawabka, F Restuccia, S D’Oro, T Jian, BC Rendon, N Soltani, J Dy, ...
IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 646-655, 2020
VMM-based intrusion detection system
M Moffie, D Kaeli, A Cohen, J Aslam, M Alshawabkeh, J Dy, F Azmandian
US Patent 8,719,936, 2014
A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology
S Patel, R Hughes, T Hester, J Stein, M Akay, JG Dy, P Bonato
Proceedings of the IEEE 98 (3), 450-461, 2010
Learning from multiple annotators with varying expertise
Y Yan, R Rosales, G Fung, R Subramanian, J Dy
Machine learning 95, 291-327, 2014
COPDGeneŽ 2019: redefining the diagnosis of chronic obstructive pulmonary disease
KE Lowe, EA Regan, A Anzueto, E Austin, JHM Austin, TH Beaty, ...
Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation 6 (5 …, 2019
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