James R. Williamson
James R. Williamson
Technical Staff in Human Health & Performance Systems, Lincoln Laboratory
Verified email at
Cited by
Cited by
Gaussian ARTMAP: A neural network for fast incremental learning of noisy multidimensional maps
JR Williamson
Neural networks 9 (5), 881-897, 1996
Seizure prediction using EEG spatiotemporal correlation structure
JR Williamson, DW Bliss, DW Browne, JT Narayanan
Epilepsy & behavior 25 (2), 230-238, 2012
A neural model of how horizontal and interlaminar connections of visual cortex develop into adult circuits that carry out perceptual grouping and learning
S Grossberg, JR Williamson
Cerebral cortex 11 (1), 37-58, 2001
Vocal and facial biomarkers of depression based on motor incoordination and timing
JR Williamson, TF Quatieri, BS Helfer, G Ciccarelli, DD Mehta
Proceedings of the 4th international workshop on audio/visual emotion …, 2014
Vocal biomarkers of depression based on motor incoordination
JR Williamson, TF Quatieri, BS Helfer, R Horwitz, B Yu, DD Mehta
Proceedings of the 3rd ACM international workshop on Audio/visual emotion …, 2013
Detecting Depression using Vocal, Facial and Semantic Communication Cues
JR Williamson, E Godoy, M Cha, A Schwarzentruber, P Khorrami, Y Gwon, ...
AVEC '16 Proceedings of the 6th International Workshop on Audio/Visual …, 2016
Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation
S Grossberg, E Mingolla, J Williamson
Neural Networks 8 (7-8), 1005-1028, 1995
On the relative importance of vocal source, system, and prosody in human depression
R Horwitz, TF Quatieri, BS Helfer, B Yu, JR Williamson, J Mundt
2013 IEEE international conference on body sensor networks, 1-6, 2013
Tracking depression severity from audio and video based on speech articulatory coordination
JR Williamson, D Young, AA Nierenberg, J Niemi, BS Helfer, TF Quatieri
Computer Speech & Language 55, 40-56, 2019
Classification of depression state based on articulatory precision.
BS Helfer, TF Quatieri, JR Williamson, DD Mehta, R Horwitz, B Yu
Interspeech, 2172-2176, 2013
A constructive, incremental-learning network for mixture modeling and classification
JR Williamson
Neural Computation 9 (7), 1517-1543, 1997
Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG
JR Williamson, DW Bliss, DW Browne
2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011
A self-organizing neural system for learning to recognize textured scenes
S Grossberg, JR Williamson
Vision Research 39 (7), 1385-1406, 1999
A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data
D Muchoney, J Williamson
IEEE transactions on Geoscience and Remote Sensing 39 (9), 1969-1977, 2001
Segment-dependent dynamics in predicting parkinson's disease.
JR Williamson, TF Quatieri, BS Helfer, J Perricone, SS Ghosh, ...
Interspeech, 518-522, 2015
Cognitive impairment prediction in the elderly based on vocal biomarkers
B Yu, TF Quatieri, JR Williamson, JC Mundt
Sixteenth Annual Conference of the International Speech Communication …, 2015
Self-organization of topographic mixture networks using attentional feedback
JR Williamson
Neural Computation 13 (3), 563-593, 2001
Generalized two-stage rank regression framework for depression score prediction from speech
N Cummins, V Sethu, J Epps, JR Williamson, TF Quatieri, J Krajewski
IEEE Transactions on Affective Computing 11 (2), 272-283, 2017
Assessing disorders through speech and a computational model
TF Quatieri, GA Ciccarelli, SS Ghosh, CJ Smalt, JR Williamson, JS Palmer
US Patent 10,127,929, 2018
Individualized apnea prediction in preterm infants using cardio-respiratory and movement signals
JR Williamson, DW Bliss, DA Browne, P Indic, E Bloch-Salisbury, ...
Body Sensor Networks (BSN), 2013 IEEE International Conference on, 1-6, 2013
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