David E. Carlson

Publication Activity (10 Years)

Years Active: 1980-2024
Publications (10 Years): 36

Top Topics

Belief Networks

Factor Analysis

Incorporating Prior Knowledge

Top Venues

Publications

Lei Duan, Ziyang Jiang, David E. Carlson
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation. CoRR (2024)
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)
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 (2023)
Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson
Estimating Causal Effects using a Multi-task Deep Ensemble. ICML (2023)
Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson
Estimating Causal Effects using a Multi-task Deep Ensemble. CoRR (2023)
Ziyang Jiang, Tongshu Zheng, David E. Carlson
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel. CoRR (2022)
Tianhui Zhou, William E. Carson IV, David E. Carlson
Estimating Potential Outcome Distributions with Collaborating Causal Networks. Trans. Mach. Learn. Res. 2022 (2022)
Siyang Yuan, Yitong Li, Dong Wang, Ke Bai, Lawrence Carin, David E. Carlson
Learning to Weight Filter Groups for Robust Classification. WACV (2022)
Liyun Tu, Austin Talbot, Neil Gallagher, David E. Carlson
Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility. IEEE Trans. Signal Process. 70 (2022)
Liyun Tu, Austin Talbot, Neil Gallagher, David E. Carlson
Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility. CoRR (2021)
Tianhui Zhou, Yitong Li, Yuan Wu, David E. Carlson
Estimating Uncertainty Intervals from Collaborating Networks. J. Mach. Learn. Res. 22 (2021)
Tianhui Zhou, David E. Carlson
Estimating Potential Outcome Distributions with Collaborating Causal Networks. CoRR (2021)
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) (2021)
Neil Gallagher, Kafui Dzirasa, David E. Carlson
Directed Spectrum Measures Improve Latent Network Models Of Neural Populations. NeurIPS (2021)
Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David E. Carlson, Lawrence Carin
Dynamic Embedding on Textual Networks via a Gaussian Process. AAAI (2020)
Austin Talbot, David B. Dunson, Kafui Dzirasa, David E. Carlson
Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. CoRR (2020)
Tianhui Zhou, Yitong Li, Yuan Wu, David E. Carlson
Estimating Uncertainty Intervals from Collaborating Networks. CoRR (2020)
Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson
On Target Shift in Adversarial Domain Adaptation. AISTATS (2019)
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)
Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Cheng, David E. Carlson, Lawrence Carin
Gaussian-Process-Based Dynamic Embedding for Textual Networks. CoRR (2019)
Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson
On Target Shift in Adversarial Domain Adaptation. CoRR (2019)
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 (2018)
Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin
Video Generation From Text. AAAI (2018)
Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson
Extracting Relationships by Multi-Domain Matching. NeurIPS (2018)
Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson
Targeting EEG/LFP Synchrony with Neural Nets. NIPS (2017)
Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski
Stochastic Bouncy Particle Sampler. ICML (2017)
Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson
Cross-Spectral Factor Analysis. NIPS (2017)
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)
Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin
Video Generation From Text. CoRR (2017)
Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. AISTATS (2016)
Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. 12 (5) (2016)
Zhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization. AISTATS (2016)
David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski
Partition Functions from Rao-Blackwellized Tempered Sampling. ICML (2016)
Yan Kaganovsky, Ikenna Odinaka, David E. Carlson, Lawrence Carin
Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization. AISTATS (2016)
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) (2016)
Chunyuan Li, Changyou Chen, David E. Carlson, Lawrence Carin
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks. AAAI (2016)
Zhe Gan, Ricardo Henao, David E. Carlson, Lawrence Carin
Learning Deep Sigmoid Belief Networks with Data Augmentation. AISTATS (2015)
Zhe Gan, Changyou Chen, Ricardo Henao, David E. Carlson, Lawrence Carin
Scalable Deep Poisson Factor Analysis for Topic Modeling. ICML (2015)
Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. CoRR (2015)
Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin
Deep Temporal Sigmoid Belief Networks for Sequence Modeling. CoRR (2015)
David E. Carlson, Volkan Cevher, Lawrence Carin
Stochastic Spectral Descent for Restricted Boltzmann Machines. AISTATS (2015)
Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin
Deep Temporal Sigmoid Belief Networks for Sequence Modeling. NIPS (2015)
Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin
GP Kernels for Cross-Spectrum Analysis. NIPS (2015)
David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher
Preconditioned Spectral Descent for Deep Learning. NIPS (2015)
Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
Neuroprosthetic decoder training as imitation learning. CoRR (2015)
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) (2014)
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)
David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
On the relations of LFPs & Neural Spike Trains. NIPS (2014)
Changwei Hu, Eunsu Ryu, David E. Carlson, Yingjian Wang, Lawrence Carin
Latent Gaussian Models for Topic Modeling. AISTATS (2014)
Liming Wang, David E. Carlson, Miguel R. D. Rodrigues, David Wilcox, A. Robert Calderbank, Lawrence Carin
Designed Measurements for Vector Count Data. NIPS (2013)
David E. Carlson, Vinayak A. Rao, Joshua T. Vogelstein, Lawrence Carin
Real-Time Inference for a Gamma Process Model of Neural Spiking. NIPS (2013)
Bo Chen, David E. Carlson, Lawrence Carin
On the Analysis of Multi-Channel Neural Spike Data. NIPS (2011)
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) (2011)
David E. Carlson
Bit-Oriented Data Link Control Procedures. IEEE Trans. Commun. 28 (4) (1980)