Bit-Oriented Data Link Control Procedures. - David E. Carlson (IEEE Trans. Commun., 1980)

On the Analysis of Multi-Channel Neural Spike Data. - Bo Chen 001; David E. Carlson; Lawrence Carin (NIPS, 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)

Real-Time Inference for a Gamma Process Model of Neural Spiking. - David E. Carlson; Vinayak A. Rao; Joshua T. Vogelstein; Lawrence Carin (NIPS, 2013)

Designed Measurements for Vector Count Data. - Liming Wang 004; David E. Carlson; Miguel R. D. Rodrigues; David Wilcox; A. Robert Calderbank; Lawrence Carin (NIPS, 2013)

Latent Gaussian Models for Topic Modeling. - Changwei Hu; Eunsu Ryu; David E. Carlson; Yingjian Wang 004; Lawrence Carin (AISTATS, 2014)

On the relations of LFPs & Neural Spike Trains. - David E. Carlson; Jana Schaich Borg; Kafui Dzirasa; Lawrence Carin (NIPS, 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 (NIPS, 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)

A Bregman Matrix and the Gradient of Mutual Information for Vector Poisson and Gaussian Channels. - Liming Wang 004; David Edwin Carlson; Miguel R. D. Rodrigues; A. Robert Calderbank; Lawrence Carin (IEEE Trans. Inf. Theory, 2014)

Stochastic Spectral Descent for Restricted Boltzmann Machines. - David E. Carlson; Volkan Cevher; Lawrence Carin (AISTATS, 2015)

Learning Deep Sigmoid Belief Networks with Data Augmentation. - Zhe Gan; Ricardo Henao; David E. Carlson; Lawrence Carin (AISTATS, 2015)

Scalable Deep Poisson Factor Analysis for Topic Modeling. - Zhe Gan; Changyou Chen; Ricardo Henao; David E. Carlson; Lawrence Carin (ICML, 2015)

Preconditioned Spectral Descent for Deep Learning. - David E. Carlson; Edo Collins; Ya-Ping Hsieh; Lawrence Carin; Volkan Cevher (NIPS, 2015)

Deep Temporal Sigmoid Belief Networks for Sequence Modeling. - Zhe Gan; Chunyuan Li; Ricardo Henao; David E. Carlson; Lawrence Carin (NIPS, 2015)

GP Kernels for Cross-Spectrum Analysis. - Kyle R. Ulrich; David E. Carlson; Kafui Dzirasa; Lawrence Carin (NIPS, 2015)

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks. - Chunyuan Li; Changyou Chen; David E. Carlson; Lawrence Carin (AAAI, 2016)

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. - Changyou Chen; David E. Carlson; Zhe Gan; Chunyuan Li; Lawrence Carin (AISTATS, 2016)

Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization. - Yan Kaganovsky; Ikenna Odinaka; David E. Carlson; Lawrence Carin (AISTATS, 2016)

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization. - Zhao Song 001; Ricardo Henao; David E. Carlson; Lawrence Carin (AISTATS, 2016)

Partition Functions from Rao-Blackwellized Tempered Sampling. - David E. Carlson; Patrick Stinson; Ari Pakman; Liam Paninski (ICML, 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)

Neuroprosthetic Decoder Training as Imitation Learning. - Josh Merel; David E. Carlson; Liam Paninski; John P. Cunningham (PLoS Comput. Biol., 2016)

Stochastic Bouncy Particle Sampler. - Ari Pakman; Dar Gilboa; David E. Carlson; Liam Paninski (ICML, 2017)

Cross-Spectral Factor Analysis. - Neil Gallagher; Kyle R. Ulrich; Austin Talbot; Kafui Dzirasa; Lawrence Carin; David E. Carlson (NIPS, 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 (NIPS, 2017)

Targeting EEG/LFP Synchrony with Neural Nets. - Yitong Li 001; Michael Murias; Samantha Major; Geraldine Dawson; Kafui Dzirasa; Lawrence Carin; David E. Carlson (NIPS, 2017)

Video Generation From Text. - Yitong Li 001; Martin Renqiang Min; Dinghan Shen; David E. Carlson; Lawrence Carin (AAAI, 2018)

Extracting Relationships by Multi-Domain Matching. - Yitong Li 001; Michael Murias; Geraldine Dawson; David E. Carlson (NeurIPS, 2018)

On Target Shift in Adversarial Domain Adaptation. - Yitong Li 001; Michael Murias; Samantha Major; Geraldine Dawson; David E. Carlson (AISTATS, 2019)

StoryGAN: A Sequential Conditional GAN for Story Visualization. - Yitong Li 001; Zhe Gan; Yelong Shen; Jingjing Liu 001; Yu Cheng 001; Yuexin Wu; Lawrence Carin; David E. Carlson; Jianfeng Gao 001 (CVPR, 2019)

Gaussian-Process-Based Dynamic Embedding for Textual Networks. - Pengyu Cheng; Yitong Li 001; Xinyuan Zhang 001; Liqun Cheng; David E. Carlson; Lawrence Carin (CoRR, 2019)

Dynamic Embedding on Textual Networks via a Gaussian Process. - Pengyu Cheng; Yitong Li 001; Xinyuan Zhang 001; Liqun Chen 001; David E. Carlson; Lawrence Carin (AAAI, 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 (MLHC, 2020)

Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. - Austin Talbot; David B. Dunson; Kafui Dzirasa; David E. Carlson (CoRR, 2020)

Directed Spectrum Measures Improve Latent Network Models Of Neural Populations. - Neil Gallagher; Kafui Dzirasa; David E. Carlson (NeurIPS, 2021)

Estimating Potential Outcome Distributions with Collaborating Causal Networks. - Tianhui Zhou; David E. Carlson (CoRR, 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 Uncertainty Intervals from Collaborating Networks. - Tianhui Zhou; Yitong Li 001; Yuan Wu; David E. Carlson (J. Mach. Learn. Res., 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 002; David E. Carlson (Remote. Sens., 2021)

Learning to Weight Filter Groups for Robust Classification. - Siyang Yuan; Yitong Li 001; Dong Wang 037; Ke Bai 001; Lawrence Carin; David E. Carlson (WACV, 2022)

AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models. - William E. Carson IV; Austin Talbot; David E. Carlson (CoRR, 2022)

Multiple Domain Causal Networks. - Tianhui Zhou; William E. Carson IV; Michael Hunter Klein; David E. Carlson (CoRR, 2022)

Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel. - Ziyang Jiang; Tongshu Zheng; David E. Carlson (CoRR, 2022)

Estimating Potential Outcome Distributions with Collaborating Causal Networks. - Tianhui Zhou; William E. Carson IV; David E. Carlson (Trans. Mach. Learn. Res., 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 Causal Effects using a Multi-task Deep Ensemble. - Ziyang Jiang; Zhuoran Hou; Yiling Liu; Yiman Ren; Keyu Li; David E. Carlson (ICML, 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)

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)

Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation. - Lei Duan; Ziyang Jiang; David E. Carlson (CoRR, 2024)

Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential. - Ian Hough; Loïc Argentier; Ziyang Jiang; Tongshu Zheng; Mike Bergin; David E. Carlson; Jean-Luc Jaffrezo; Jocelyn Chanussot; Gaëlle Uzu (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)

Generative Principal Component Regression via Variational Inference. - Austin Talbot; Corey J. Keller; David E. Carlson; Alex V. Kotlar (CoRR, 2024)

Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments. - Ziyang Jiang; Zach Calhoun; Yiling Liu; Lei Duan; David E. Carlson (CoRR, 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)

Efficient few-shot medical image segmentation via self-supervised variational autoencoder. - Yanjie Zhou; Feng Zhou; Fengjun Xi; Yong Liu; Yun Peng; David E. Carlson; Liyun Tu (Medical Image Anal., 2025)

Scale-free and unbiased transformer with tokenization for cell type annotation from single-cell RNA-seq data. - Hongwei Zhang; Ziyang Jiang; Song Zhang; Liyun Tu; David E. Carlson (Pattern Recognit., 2025)

Understanding and Robustifying Sub-domain Alignment for Domain Adaptation. - Yiling Liu; Juncheng Dong; Ziyang Jiang; Ahmed Aloui; Keyu Li; Michael Hunter Klein; Vahid Tarokh; David E. Carlson (Trans. Mach. Learn. Res., 2025)

Pose Splatter: A 3D Gaussian Splatting Model for Quantifying Animal Pose and Appearance. - Jack Goffinet; Youngjo Min; Carlo Tomasi; David E. Carlson (CoRR, 2025)