David E. Carlson

According to our database, David E. Carlson authored at least 53 papers between 1980 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel.

Ziyang Jiang, Tongshu Zheng, Yiling Liu, David E. Carlson

Trans. Mach. Learn. Res., 2024

Generative Principal Component Regression via Variational Inference.

Austin Talbot, Corey J. Keller, David E. Carlson, Alex V. Kotlar

CoRR, 2024

Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation.

Feng Zhou, Yanjie Zhou, Longjie Wang, Yun Peng, David E. Carlson, Liyun Tu

CoRR, 2024

Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation.

Lei Duan, Ziyang Jiang, David E. Carlson

CoRR, 2024

2023

Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators.

Ziyang Jiang, Yiling Liu, Michael Hunter Klein, Ahmed Aloui, Yiman Ren, Keyu Li, Vahid Tarokh, David E. Carlson

CoRR, 2023

Domain Adaptation via Rebalanced Sub-domain Alignment.

Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David E. Carlson

CoRR, 2023

Estimating Causal Effects using a Multi-task Deep Ensemble.

Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson

Proceedings of the International Conference on Machine Learning, 2023

2022

Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility.

Liyun Tu, Austin Talbot, Neil Gallagher, David E. Carlson

IEEE Trans. Signal Process., 2022

Estimating Potential Outcome Distributions with Collaborating Causal Networks.

Tianhui Zhou, William E. Carson IV, David E. Carlson

Trans. Mach. Learn. Res., 2022

Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel.

Ziyang Jiang, Tongshu Zheng, David E. Carlson

CoRR, 2022

Multiple Domain Causal Networks.

Tianhui Zhou, William E. Carson IV, Michael Hunter Klein, David E. Carlson

CoRR, 2022

AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models.

William E. Carson IV, Austin Talbot, David E. Carlson

CoRR, 2022

Learning to Weight Filter Groups for Robust Classification.

Siyang Yuan, Yitong Li, Dong Wang, Ke Bai, Lawrence Carin, David E. Carlson

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

2021

Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest-Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology.

Tongshu Zheng, Michael Bergin, Guoyin Wang, David E. Carlson

Remote. Sens., 2021

Estimating Uncertainty Intervals from Collaborating Networks.

Tianhui Zhou, Yitong Li, Yuan Wu, David E. Carlson

J. Mach. Learn. Res., 2021

Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers.

William E. Carson IV, Dmitry Yu. Isaev, Samantha Major, Guillermo Sapiro, Geraldine Dawson, David E. Carlson

CoRR, 2021

Estimating Potential Outcome Distributions with Collaborating Causal Networks.

Tianhui Zhou, David E. Carlson

CoRR, 2021

Directed Spectrum Measures Improve Latent Network Models Of Neural Populations.

Neil Gallagher, Kafui Dzirasa, David E. Carlson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity.

Austin Talbot, David B. Dunson, Kafui Dzirasa, David E. Carlson

CoRR, 2020

Attention-Based Network for Weak Labels in Neonatal Seizure Detection.

Dmitry Yu. Isaev, Dmitry Tchapyjnikov, C. Michael Cotten, David Tanaka, Natalia Martínez, Martín Bertrán, Guillermo Sapiro, David E. Carlson

Proceedings of the Machine Learning for Healthcare Conference, 2020

Dynamic Embedding on Textual Networks via a Gaussian Process.

Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David E. Carlson, Lawrence Carin

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Gaussian-Process-Based Dynamic Embedding for Textual Networks.

Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Cheng, David E. Carlson, Lawrence Carin

CoRR, 2019

StoryGAN: A Sequential Conditional GAN for Story Visualization.

Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David E. Carlson, Jianfeng Gao

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

On Target Shift in Adversarial Domain Adaptation.

Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Extracting Relationships by Multi-Domain Matching.

Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Video Generation From Text.

Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Targeting EEG/LFP Synchrony with Neural Nets.

Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

YASS: Yet Another Spike Sorter.

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Cross-Spectral Factor Analysis.

Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stochastic Bouncy Particle Sampler.

Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Neuroprosthetic Decoder Training as Imitation Learning.

Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham

PLoS Comput. Biol., 2016

Stochastic Spectral Descent for Discrete Graphical Models.

David E. Carlson, Ya-Ping Hsieh, Edo Collins, Lawrence Carin, Volkan Cevher

IEEE J. Sel. Top. Signal Process., 2016

Partition Functions from Rao-Blackwellized Tempered Sampling.

David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

Proceedings of the 33nd International Conference on Machine Learning, 2016

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization.

Zhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization.

Yan Kaganovsky, Ikenna Odinaka, David E. Carlson, Lawrence Carin

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization.

Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks.

Chunyuan Li, Changyou Chen, David E. Carlson, Lawrence Carin

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

GP Kernels for Cross-Spectrum Analysis.

Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Deep Temporal Sigmoid Belief Networks for Sequence Modeling.

Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Preconditioned Spectral Descent for Deep Learning.

David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Scalable Deep Poisson Factor Analysis for Topic Modeling.

Zhe Gan, Changyou Chen, Ricardo Henao, David E. Carlson, Lawrence Carin

Proceedings of the 32nd International Conference on Machine Learning, 2015

Learning Deep Sigmoid Belief Networks with Data Augmentation.

Zhe Gan, Ricardo Henao, David E. Carlson, Lawrence Carin

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Stochastic Spectral Descent for Restricted Boltzmann Machines.

David E. Carlson, Volkan Cevher, Lawrence Carin

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

A Bregman Matrix and the Gradient of Mutual Information for Vector Poisson and Gaussian Channels.

Liming Wang, David Edwin Carlson, Miguel R. D. Rodrigues, A. Robert Calderbank, Lawrence Carin

IEEE Trans. Inf. Theory, 2014

Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling.

David E. Carlson, Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, Lawrence Carin

IEEE Trans. Biomed. Eng., 2014

Analysis of Brain States from Multi-Region LFP Time-Series.

Kyle R. Ulrich, David E. Carlson, Wenzhao Lian, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On the relations of LFPs & Neural Spike Trains.

David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Latent Gaussian Models for Topic Modeling.

Changwei Hu, Eunsu Ryu, David E. Carlson, Yingjian Wang, Lawrence Carin

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Designed Measurements for Vector Count Data.

Liming Wang, David E. Carlson, Miguel R. D. Rodrigues, David Wilcox, A. Robert Calderbank, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Real-Time Inference for a Gamma Process Model of Neural Spiking.

David E. Carlson, Vinayak A. Rao, Joshua T. Vogelstein, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2011

Detection of Viruses Via Statistical Gene Expression Analysis.

Minhua Chen, David E. Carlson, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred O. Hero III, Joseph E. Lucas, Lawrence Carin

IEEE Trans. Biomed. Eng., 2011

On the Analysis of Multi-Channel Neural Spike Data.

Bo Chen, David E. Carlson, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

1980

Bit-Oriented Data Link Control Procedures.

David E. Carlson

IEEE Trans. Commun., 1980