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Virginia R. de Sa
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2020 – today
- 2024
- [i15]Teng Fei, Virginia de Sa:
Image Reconstruction from Electroencephalography Using Latent Diffusion. CoRR abs/2404.01250 (2024) - [i14]Shuangquan Feng, Virginia R. de Sa:
One-Frame Calibration with Siamese Network in Facial Action Unit Recognition. CoRR abs/2409.00240 (2024) - 2023
- [c39]Vijay Veerabadran, Srinivas Ravishankar, Yuan Tang, Ritik Raina, Virginia de Sa:
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels. NeurIPS 2023 - [i13]Vijay Veerabadran, Srinivas Ravishankar, Yuan Tang, Ritik Raina, Virginia R. de Sa:
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels. CoRR abs/2311.06964 (2023) - [i12]Shuangquan Feng, Junhua Ma, Virginia R. de Sa:
FERGI: Automatic Annotation of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction. CoRR abs/2312.03187 (2023) - 2022
- [j16]H. Nakamura, Ken Uchibori, Shigeo Kawata
, Takahiro Karino, Virginia R. de Sa, Aleksandar I. Ogoyski:
Code O-SUKI-N 3D: Upgraded direct-drive fuel target 3D implosion code in heavy ion inertial fusion. Comput. Phys. Commun. 272: 108223 (2022) - [j15]Busra T. Susam
Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning. IEEE Trans. Biomed. Eng. 69(1): 422-431 (2022) - [c38]Zhining Chen, Mahta Mousavi, Virginia R. de Sa:
Multi-Subject Unsupervised Transfer with Weighted Subspace Alignment for Common Spatial Patterns. BCI 2022: 1-6 - [c37]Alessandro D'Amico, Virginia de Sa:
Set Size Effects on the P3b in a BCI Speller. CogSci 2022 - [c36]Sarah Fabi, Xiaojing Xu, Virginia de Sa:
Exploring the Racial Bias in Pain Detection with a Computer Vision Model. CogSci 2022 - [i11]Mahta Mousavi, Eric Lybrand, Shuangquan Feng, Shuai Tang, Rayan Saab, Virginia de Sa:
Spectrally Adaptive Common Spatial Patterns. CoRR abs/2202.04542 (2022) - 2021
- [c35]Kueida Liao, Matthew V. Mollison, Tim Curran, Virginia de Sa:
EEG Reveals Familiarity by Controlling Confidence in Memory Retrieval. CogSci 2021 - [c34]Xiaojing Xu, Virginia R. de Sa:
Personalized Pain Detection in Facial Video with Uncertainty Estimation. EMBC 2021: 4163-4168 - [c33]Mahta Mousavi, Virginia R. de Sa:
Motor imagery performance from calibration to online control in EEG-based brain-computer interfaces. NER 2021: 491-494 - 2020
- [c32]Xiaojing Xu, Virginia R. de Sa:
Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units. FG 2020: 786-792 - [i10]Shuai Tang, Virginia R. de Sa:
Deep Transfer Learning with Ridge Regression. CoRR abs/2006.06791 (2020) - [i9]Vijay Veerabadran, Virginia R. de Sa:
Learning compact generalizable neural representations supporting perceptual grouping. CoRR abs/2006.11716 (2020)
2010 – 2019
- 2019
- [c31]Shuai Tang, Virginia R. de Sa:
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning. ACL (1) 2019: 4050-4060 - [c30]Mahta Mousavi
Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks. EMBC 2019: 4533-4536 - [c29]Xiaojing Xu, Jeannie S. Huang, Virginia R. de Sa:
Pain Evaluation in Video using Extended Multitask Learning from Multidimensional Measurements. ML4H@NeurIPS 2019: 141-154 - [i8]Shuai Tang, Mahta Mousavi, Virginia R. de Sa:
An Empirical Study on Post-processing Methods for Word Embeddings. CoRR abs/1905.10971 (2019) - 2018
- [c28]Yiming Jin, Mahta Mousavi
Adaptive CSP with subspace alignment for subject-to-subject transfer in motor imagery brain-computer interfaces. BCI 2018: 1-4 - [c27]Kueida Liao, Matthew V. Mollison, Tim Curran, Virginia R. de Sa:
Single-Trial EEG Predicts Memory Retrieval Using Leave-One-Subject-Out Classification. BIBM 2018: 2613-2620 - [c26]Busra T. Susam
Automated Pain Assessment using Electrodermal Activity Data and Machine Learning. EMBC 2018: 372-375 - [c25]Xiaojing Xu, Kenneth Craig, Damaris Diaz, Matthew S. Goodwin, Murat Akçakaya, Busra Susam, Jeannie Huang, Virginia R. de Sa:
Automated pain detection in facial videos of children using human-assisted transfer learning. AIH@IJCAI 2018: 10-21 - [c24]Xiaojing Xu
Automated Pain Detection in Facial Videos of Children Using Human-Assisted Transfer Learning. AIH@IJCAI (Revised Selected Papers) 2018: 162-180 - [c23]Xiaojing Xu
Towards Automated Pain Detection in Children Using Facial and Electrodermal Activity. AIH@IJCAI (Revised Selected Papers) 2018: 181-189 - [c22]Xiaojing Xu, Busra Susam, Hooman Nezamfar, Kenneth Craig, Damaris Diaz, Jeannie Huang, Matthew S. Goodwin, Murat Akçakaya, Virginia R. de Sa:
Towards automated pain detection in children using facial and electrodermal activity. AIH@IJCAI 2018: 208-211 - [c21]Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa:
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding. Rep4NLP@ACL 2018: 69-78 - [c20]Laurens R. Krol
Towards Classifier Visualisation in 3D Source Space. SMC 2018: 71-76 - [i7]Shuai Tang, Virginia R. de Sa:
Multi-view Sentence Representation Learning. CoRR abs/1805.07443 (2018) - [i6]Shuai Tang, Virginia R. de Sa:
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning. CoRR abs/1809.02731 (2018) - [i5]Shuai Tang, Virginia R. de Sa:
Improving Sentence Representations with Multi-view Frameworks. CoRR abs/1810.01064 (2018) - [i4]Shuai Tang, Paul Smolensky, Virginia R. de Sa:
Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations. CoRR abs/1810.12456 (2018) - 2017
- [c19]Ramesh Maddula, Joshua Stivers, Mahta Mousavi, Sriram Ravindran, Virginia de Sa:
Deep Recurrent Convolutional Neural Networks for Classifying P300 BCI signals. GBCIC 2017 - [c18]Daniel Maryanovsky, Mahta Mousavi, Nathanial Moreno, Virginia de Sa:
Csp-NN: a Convolutional Neural Network Implementation of Common Spatial Patterns. GBCIC 2017 - [c17]Mahta Mousavi, Virginia de Sa:
Towards Elaborated feedback for Training motor imagery brain Computer Interfaces. GBCIC 2017 - [c16]Virginia de Sa:
Improving Information Transfer rate in Active BCIs. GBCIC 2017 - [c15]Joshua Stivers, Virginia de Sa:
Spelling in Parallel: towards a Rapid, spatially Independent BCI. GBCIC 2017 - [c14]Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa:
Rethinking Skip-thought: A Neighborhood based Approach. Rep4NLP@ACL 2017: 211-218 - [i3]Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa:
Rethinking Skip-thought: A Neighborhood based Approach. CoRR abs/1706.03146 (2017) - [i2]Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa:
Trimming and Improving Skip-thought Vectors. CoRR abs/1706.03148 (2017) - [i1]Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa:
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning. CoRR abs/1710.10380 (2017) - 2014
- [j14]Eunho Noh, Grit Herzmann, Tim Curran, Virginia R. de Sa:
Using single-trial EEG to predict and analyze subsequent memory. NeuroImage 84: 712-723 (2014) - [c13]Eunho Noh, Matthew V. Mollison, Tim Curran, Virginia R. de Sa:
Single-trial identification of failed memory retrieval. ACSSC 2014: 21-25 - [c12]Eunho Noh, Virginia R. de Sa:
Discriminative dimensionality reduction for analyzing EEG data. CogSci 2014 - 2013
- [j13]Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten:
Divvy: fast and intuitive exploratory data analysis. J. Mach. Learn. Res. 14(1): 3159-3163 (2013) - 2012
- [c11]Joshua M. Lewis, Margareta Ackerman, Virginia R. de Sa:
Human Cluster Evaluation and Formal Quality Measures: A Comparative Study. CogSci 2012 - [c10]Joshua M. Lewis, Laurens van der Maaten, Virginia R. de Sa:
A Behavioral Investigation of Dimensionality Reduction. CogSci 2012 - [c9]Joshua M. Lewis, Virginia R. de Sa:
Using Machine Learning for Exploratory Data Analysis. CogSci 2012 - [c8]Joshua M. Lewis, Virginia R. de Sa:
Learning Cluster Analysis through Experience. CogSci 2012 - [c7]Virginia R. de Sa:
An interactive control strategy is more robust to non-optimal classification boundaries. ICMI 2012: 579-586 - 2011
- [c6]Walter A. Talbott, Ian R. Fasel, Javier Molina, Virginia R. de Sa, Javier R. Movellan:
Coordinating Touch and Vision to Learn What Objects Look Like. CogSci 2011 - 2010
- [j12]Virginia R. de Sa, Patrick W. Gallagher, Joshua M. Lewis, Vicente L. Malave:
Multi-view kernel construction. Mach. Learn. 79(1-2): 47-71 (2010)
2000 – 2009
- 2008
- [j11]Thomas J. Sullivan, Virginia R. de Sa:
Sleeping Our Way to Weight Normalization and Stable Learning. Neural Comput. 20(12): 3111-3130 (2008) - [j10]Paul S. Hammon, Scott Makeig, Howard Poizner, Emanuel Todorov, Virginia R. de Sa:
Predicting Reaching Targets from Human EEG. IEEE Signal Process. Mag. 25(1): 69-77 (2008) - 2007
- [j9]Paul S. Hammon, Virginia R. de Sa:
Preprocessing and Meta-Classification for Brain-Computer Interfaces. IEEE Trans. Biomed. Eng. 54(3): 518-525 (2007) - 2006
- [j8]Thomas J. Sullivan, Virginia R. de Sa:
A self-organizing map with homeostatic synaptic scaling. Neurocomputing 69(10-12): 1183-1186 (2006) - [j7]Thomas J. Sullivan, Virginia R. de Sa:
A model of surround suppression through cortical feedback. Neural Networks 19(5): 564-572 (2006) - [j6]Thomas J. Sullivan, Virginia R. de Sa:
Homeostatic synaptic scaling in self-organizing maps. Neural Networks 19(6-7): 734-743 (2006) - 2004
- [j5]Thomas J. Sullivan, Virginia R. de Sa:
A temporal trace and SOM-based model of complex cell development. Neurocomputing 58-60: 827-833 (2004) - [j4]Hsin-Hao Yu, Virginia R. de Sa:
Nonlinear reverse correlation with synthesized naturalistic noise. Neurocomputing 58-60: 909-913 (2004) - 2003
- [j3]Rich Caruana, Virginia R. de Sa:
Benefitting from the Variables that Variable Selection Discards. J. Mach. Learn. Res. 3: 1245-1264 (2003) - [c5]Virginia R. de Sa:
Sensory Modality Segregation. NIPS 2003: 913-920 - 2001
- [j2]Virginia R. de Sa, David J. C. MacKay:
Model fitting as an aid to bridge balancing in neuronal recording. Neurocomputing 38-40: 1651-1656 (2001)
1990 – 1999
- 1998
- [j1]Virginia R. de Sa, Dana H. Ballard:
Category Learning Through Multi-Modality Sensing. Neural Comput. 10(5): 1097-1117 (1998) - 1997
- [c4]Virginia R. de Sa, R. Christopher DeCharms, Michael Merzenich:
Using Helmholtz Machines to Analyze Multi-channel Neuronal Recordings. NIPS 1997: 131-137 - 1996
- [c3]Rich Caruana, Virginia R. de Sa:
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs. NIPS 1996: 389-395 - 1993
- [c2]Virginia R. de Sa:
Learning Classification with Unlabeled Data. NIPS 1993: 112-119 - 1992
- [c1]Virginia R. de Sa, Dana H. Ballard:
A Note on Learning Vector Quantization. NIPS 1992: 220-227
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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