Jennifer Dy
Jennifer Dy
Electrical and Computer Engineering, Northeastern University
Adresse e-mail validée de
Citée par
Citée par
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
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
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
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
Deep learning for RF fingerprinting: A massive experimental study
T Jian, BC Rendon, E Ojuba, N Soltani, Z Wang, K Sankhe, A Gritsenko, ...
IEEE Internet of Things Magazine 3 (1), 50-57, 2020
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–20