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2020 – today
- 2024
[j11]Ziyang Jiang, Tongshu Zheng, Yiling Liu, David E. Carlson:
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel. Trans. Mach. Learn. Res. 2024 (2024)- [i21]Lei Duan, Ziyang Jiang, David E. Carlson:
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation. CoRR abs/2401.08061 (2024) - [i20]Feng Zhou, Yanjie Zhou, Longjie Wang, Yun Peng, David E. Carlson, Liyun Tu:
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation. CoRR abs/2408.03616 (2024) - [i19]Austin Talbot, Corey J. Keller, David E. Carlson, Alex V. Kotlar:
Generative Principal Component Regression via Variational Inference. CoRR abs/2409.02327 (2024) - 2023
- [c30]Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson:
Estimating Causal Effects using a Multi-task Deep Ensemble. ICML 2023: 15023-15040 - [i18]Ziyang Jiang, Zhuoran Hou
, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson:
Estimating Causal Effects using a Multi-task Deep Ensemble. CoRR abs/2301.11351 (2023) - [i17]Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David E. Carlson:
Domain Adaptation via Rebalanced Sub-domain Alignment. CoRR abs/2302.02009 (2023) - [i16]Ziyang Jiang, Yiling Liu, Michael Hunter Klein, Ahmed Aloui, Yiman Ren, Keyu Li, Vahid Tarokh, David E. Carlson:
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators. CoRR abs/2306.07918 (2023) - 2022
- [j10]Tianhui Zhou, William E. Carson IV, David E. Carlson:
Estimating Potential Outcome Distributions with Collaborating Causal Networks. Trans. Mach. Learn. Res. 2022 (2022) - [j9]Liyun Tu, Austin Talbot, Neil Gallagher, David E. Carlson:
Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility. IEEE Trans. Signal Process. 70: 5954-5966 (2022) - [c29]Siyang Yuan, Yitong Li, Dong Wang, Ke Bai, Lawrence Carin, David E. Carlson:
Learning to Weight Filter Groups for Robust Classification. WACV 2022: 3321-3330 - [i15]William E. Carson IV, Austin Talbot, David E. Carlson:
AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models. CoRR abs/2201.02547 (2022) - [i14]Tianhui Zhou, William E. Carson IV, Michael Hunter Klein, David E. Carlson:
Multiple Domain Causal Networks. CoRR abs/2205.06791 (2022) - [i13]Ziyang Jiang, Tongshu Zheng, David E. Carlson:
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel. CoRR abs/2205.07384 (2022) - 2021
- [j8]Tianhui Zhou, Yitong Li, Yuan Wu, David E. Carlson:
Estimating Uncertainty Intervals from Collaborating Networks. J. Mach. Learn. Res. 22: 257:1-257:47 (2021) - [j7]Tongshu Zheng, Michael Bergin, Guoyin Wang, David E. Carlson:
Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest-Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology. Remote. Sens. 13(7): 1356 (2021) - [c28]Neil Gallagher, Kafui Dzirasa, David E. Carlson:
Directed Spectrum Measures Improve Latent Network Models Of Neural Populations. NeurIPS 2021: 7421-7435 - [i12]Liyun Tu, Austin Talbot, Neil Gallagher, David E. Carlson:
Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility. CoRR abs/2109.04561 (2021) - [i11]Tianhui Zhou, David E. Carlson:
Estimating Potential Outcome Distributions with Collaborating Causal Networks. CoRR abs/2110.01664 (2021) - [i10]William E. Carson IV, Dmitry Yu. Isaev, Samantha Major, Guillermo Sapiro, Geraldine Dawson, David E. Carlson:
Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers. CoRR abs/2111.15347 (2021) - 2020
- [c27]Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David E. Carlson, Lawrence Carin:
Dynamic Embedding on Textual Networks via a Gaussian Process. AAAI 2020: 7562-7569 - [c26]Dmitry Yu. Isaev, Dmitry Tchapyjnikov, C. Michael Cotten, David Tanaka, Natalia Martínez, Martín Bertrán, Guillermo Sapiro, David E. Carlson:
Attention-Based Network for Weak Labels in Neonatal Seizure Detection. MLHC 2020: 479-507 - [i9]Tianhui Zhou, Yitong Li, Yuan Wu, David E. Carlson:
Estimating Uncertainty Intervals from Collaborating Networks. CoRR abs/2002.05212 (2020) - [i8]Austin Talbot, David B. Dunson, Kafui Dzirasa, David E. Carlson:
Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. CoRR abs/2004.05209 (2020)
2010 – 2019
- 2019
- [c25]Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson:
On Target Shift in Adversarial Domain Adaptation. AISTATS 2019: 616-625 - [c24]Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David E. Carlson, Jianfeng Gao:
StoryGAN: A Sequential Conditional GAN for Story Visualization. CVPR 2019: 6329-6338 - [i7]Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson:
On Target Shift in Adversarial Domain Adaptation. CoRR abs/1903.06336 (2019) - [i6]Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Cheng, David E. Carlson, Lawrence Carin:
Gaussian-Process-Based Dynamic Embedding for Textual Networks. CoRR abs/1910.02187 (2019) - 2018
- [c23]Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin:
Video Generation From Text. AAAI 2018: 7065-7072 - [c22]Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson:
Extracting Relationships by Multi-Domain Matching. NeurIPS 2018: 6799-6810 - [i5]Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David E. Carlson, Jianfeng Gao:
StoryGAN: A Sequential Conditional GAN for Story Visualization. CoRR abs/1812.02784 (2018) - 2017
- [c21]Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski:
Stochastic Bouncy Particle Sampler. ICML 2017: 2741-2750 - [c20]Jin Hyung Lee, David E. Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A. Goetz, Espen Hagen, Eleanor Batty, E. J. Chichilnisky, Gaute T. Einevoll, Liam Paninski:
YASS: Yet Another Spike Sorter. NIPS 2017: 4002-4012 - [c19]Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson:
Targeting EEG/LFP Synchrony with Neural Nets. NIPS 2017: 4620-4630 - [c18]Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson:
Cross-Spectral Factor Analysis. NIPS 2017: 6842-6852 - [i4]Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin:
Video Generation From Text. CoRR abs/1710.00421 (2017) - 2016
- [j6]David E. Carlson, Ya-Ping Hsieh, Edo Collins, Lawrence Carin, Volkan Cevher:
Stochastic Spectral Descent for Discrete Graphical Models. IEEE J. Sel. Top. Signal Process. 10(2): 296-311 (2016) - [j5]Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham:
Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. 12(5) (2016) - [c17]Chunyuan Li, Changyou Chen, David E. Carlson, Lawrence Carin:
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks. AAAI 2016: 1788-1794 - [c16]Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin:
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. AISTATS 2016: 1051-1060 - [c15]Zhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin:
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization. AISTATS 2016: 1347-1355 - [c14]Yan Kaganovsky, Ikenna Odinaka, David E. Carlson, Lawrence Carin:
Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization. AISTATS 2016: 1497-1505 - [c13]David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski:
Partition Functions from Rao-Blackwellized Tempered Sampling. ICML 2016: 2896-2905 - 2015
- [c12]David E. Carlson, Volkan Cevher, Lawrence Carin:
Stochastic Spectral Descent for Restricted Boltzmann Machines. AISTATS 2015 - [c11]Zhe Gan, Ricardo Henao, David E. Carlson, Lawrence Carin:
Learning Deep Sigmoid Belief Networks with Data Augmentation. AISTATS 2015 - [c10]Zhe Gan, Changyou Chen, Ricardo Henao, David E. Carlson, Lawrence Carin:
Scalable Deep Poisson Factor Analysis for Topic Modeling. ICML 2015: 1823-1832 - [c9]Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin:
GP Kernels for Cross-Spectrum Analysis. NIPS 2015: 1999-2007 - [c8]Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin:
Deep Temporal Sigmoid Belief Networks for Sequence Modeling. NIPS 2015: 2467-2475 - [c7]David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher:
Preconditioned Spectral Descent for Deep Learning. NIPS 2015: 2971-2979 - [i3]Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin:
Deep Temporal Sigmoid Belief Networks for Sequence Modeling. CoRR abs/1509.07087 (2015) - [i2]Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham:
Neuroprosthetic decoder training as imitation learning. CoRR abs/1511.04156 (2015) - [i1]Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin:
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. CoRR abs/1512.07962 (2015) - 2014
- [j4]David E. Carlson, Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, Lawrence Carin:
Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling. IEEE Trans. Biomed. Eng. 61(1): 41-54 (2014) - [j3]Liming Wang, David Edwin Carlson, Miguel R. D. Rodrigues, A. Robert Calderbank, Lawrence Carin:
A Bregman Matrix and the Gradient of Mutual Information for Vector Poisson and Gaussian Channels. IEEE Trans. Inf. Theory 60(5): 2611-2629 (2014) - [c6]Changwei Hu, Eunsu Ryu, David E. Carlson, Yingjian Wang, Lawrence Carin:
Latent Gaussian Models for Topic Modeling. AISTATS 2014: 393-401 - [c5]David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin:
On the relations of LFPs & Neural Spike Trains. NIPS 2014: 2060-2068 - [c4]Kyle R. Ulrich, David E. Carlson, Wenzhao Lian, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin:
Analysis of Brain States from Multi-Region LFP Time-Series. NIPS 2014: 2483-2491 - 2013
- [c3]Liming Wang, David E. Carlson, Miguel R. D. Rodrigues, David Wilcox, A. Robert Calderbank, Lawrence Carin:
Designed Measurements for Vector Count Data. NIPS 2013: 1142-1150 - [c2]David E. Carlson, Vinayak A. Rao, Joshua T. Vogelstein, Lawrence Carin:
Real-Time Inference for a Gamma Process Model of Neural Spiking. NIPS 2013: 2805-2813 - 2011
- [j2]Minhua Chen, David E. Carlson, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred O. Hero III, Joseph E. Lucas, Lawrence Carin:
Detection of Viruses Via Statistical Gene Expression Analysis. IEEE Trans. Biomed. Eng. 58(3): 468-479 (2011) - [c1]Bo Chen, David E. Carlson, Lawrence Carin:
On the Analysis of Multi-Channel Neural Spike Data. NIPS 2011: 936-944
1980 – 1989
- 1980
- [j1]David E. Carlson:
Bit-Oriented Data Link Control Procedures. IEEE Trans. Commun. 28(4): 455-467 (1980)
Coauthor Index
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last updated on 2024-10-22 21:17 CEST by the dblp team
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