Keyword Search
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Keyword Search Criteria: Deep learning returned 63 record(s)
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Sunday, 07/28/2019
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Deep Pixel-To-Pixel Learning for Single-Stage Nucleus Recognition in Digital Pathology Images
Fuyong Xing, University of Colorado Anschutz Medical Campus
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Artificial Intelligence for Data Science
Jason H Moore, University of Pennsylvania; Joel Dudley, Icahn School of Medicine at Mount Sinai; Larry Hunter, University of Colorodo Denver
2:05 PM
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Towards Demystifying Over-Parameterization in Deep Learning
Mahdi Soltanolkotabi, University of Southern California
2:35 PM
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On Statistical Thinking in Deep Learning
Yee Whye Teh, University of Oxford
4:05 PM
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Embedding Learning
Ben Dai, University of Minnesota; Xiaotong Shen, University of Minnesota
4:05 PM
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Deep Learning in Pathological Image Analysis
Guanghua Xiao, UT Southwestern Medical Center; Shidan Wang, UT Southwestern Medical Center
4:25 PM
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Complex Disease Risk Prediction via a Deep Learning Method
Chong Wu, Florida State University
4:45 PM
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Double Deep Learning for Adjusting Complex Confounding Structures
Xinlei Mi, Columbia University
4:50 PM
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Incorporating Biological Network to Build Deep Learning Models for Gene Expression Data
Tianwei Yu, Emory University; Yunchuan Kong, Emory University
5:05 PM
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Monday, 07/29/2019
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Predicting Traffic Intensity with Deep Learning and Semantic Segmentation
Logan Bradley-Trietsch, Purdue University; Xiao Wang, Purdue University
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Detecting Statistical Interactions via Additive Neural Network
Fan Wu, Purdue University; Tianyang Hu, Purdue Statistics
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Deep Learning and MARS: a Connection
Sophie Langer, Technische Universitaet Darmstadt; Michael Kohler, Technische Universitaet Darmstadt; Adam Krzyzak, Concordia University
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Activation Adaptation in Neural Networks
Vahid Partovi Nia, Huawei Technologies, Ecole Polytechnique de Montreal; Farnoush Farhadi, Ericsson ; Andrea Lodi, Ecole Polytechnique de Montreal
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Deep Learning and MARS: a Connection
Sophie Langer, Technische Universitaet Darmstadt; Michael Kohler, Technische Universitaet Darmstadt; Adam Krzyzak, Concordia University
8:50 AM
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Activation Adaptation in Neural Networks
Vahid Partovi Nia, Huawei Technologies, Ecole Polytechnique de Montreal; Farnoush Farhadi, Ericsson ; Andrea Lodi, Ecole Polytechnique de Montreal
9:05 AM
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Machine Learning Algorithms for Automatic Identification of Limnonectes Species Using Image Data
Li Xu, Virginia Tech; Eric Smith, Virginia Tech; Yili Hong, Virginia Tech; David McLeod, James Madison University
9:05 AM
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Stein Neural Sampler
Guang Cheng, Purdue Statistics; Tianyang Hu, Purdue Statistics; Zixiang Chen, Tsinghua Statistics; Hanxi Sun, Purdue Statistics; Jincheng Bai, Purdue Statistics; Mao Ye, Purdue Statistics
10:35 AM
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Predicting 30-Day Hospital Readmissions Using Deep Learning
Wenshuo Liu, University of Michigan-Ann Arbor; Ji Zhu, University of Michigan; Brahmajee Nallamothu, University of Michigan-Ann Arbor; Akbar Waljee, University of Michigan-Ann Arbor; Karandeep Singh, University of Michigan-Ann Arbor; Andrew Ryan, University of Michigan-Ann Arbor; Devraj Sukul, University of Michigan-Ann Arbor; Elham Mahmoudi, University of Michigan-Ann Arbor
10:50 AM
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ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion
Xiao Wang, Purdue University; Yixuan Qiu, Carnegie Mellon University
11:00 AM
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Some Statistical Insights into Deep Learning
Hao Wu, University of Southern California; Yingying Fan, University of Southern California; Jinchi Lv, University of Southern California
11:25 AM
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Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning
Tianyu Zhan, Immunology, DSS, AbbVie; Alan Hartford, Takeda Pharmaceutical Company; Jian Kang, University of Michigan; Walt Offen, Retired
2:05 PM
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Understanding the Effects of Predictor Variables in Black-Box Supervised Learning Models
Daniel W Apley, Northwestern University
2:05 PM
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Using Supervised Machine Learning to Classify Customer Input
Adrianna Steers-Smith, USDA/FSIS
3:10 PM
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011, 010111, and 011111100100
Xiao-Li Meng, Harvard University
8:05 PM
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Tuesday, 07/30/2019
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Deep Learning with GWAS to Predict AMD Progression
Tao Sun, University of Pittsburgh; Wei Chen, Children's Hospital of Pittsburgh of UPMC; Ying Ding, University of Pittsburgh
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Using Supervised Machine Learning to Classify Customer Input
Adrianna Steers-Smith, USDA/FSIS
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Statistical Optimality of Interpolated Nearest Neighbor Algorithms
Yue Xing, Purdue University; Qifan Song, Purdue University; Guang Cheng, Purdue Statistics
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Semi-Supervised, Dynamic Class-Informative Feature Learning
Vincent Pisztora
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Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior
Andrew T Karl, Adsurgo LLC; James Wisnowski, Adsurgo LLC; Lambros Petropoulos, USAA
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Survival Analysis for Medical Imaging Data
Samantha Morrison, Brown University; Jon Steingrimsson, Brown University; Constantine Gatsonis, Brown University
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Machine Learning and Deep Learning Based on Multiple View Images and Additional Information
Zheng Xu, University of Nebraska-Lincoln; Cong Wu, University of Nebraska-Lincoln
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Estimation and Inversion of Generative Networks
John Lafferty, Yale University
8:35 AM
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Semi-Supervised, Dynamic Class-Informative Feature Learning
Vincent Pisztora
8:40 AM
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Deep Learning for Real-Time Classification of Transient Time Series from Massive Astronomical Data Streams
Daniel Muthukrishna, University of Cambridge
8:55 AM
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Statistical Optimality of Interpolated Nearest Neighbor Algorithms
Yue Xing, Purdue University; Qifan Song, Purdue University; Guang Cheng, Purdue Statistics
9:05 AM
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Predict Phase 3 Clinical Trial Results Using Phase 2 Data and Electronic Health Records
Qi Tang, Sanofi; Youran Qi, University of Wisconsin
9:15 AM
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Machine Learning and Deep Learning Based on Multiple View Images and Additional Information
Zheng Xu, University of Nebraska-Lincoln; Cong Wu, University of Nebraska-Lincoln
9:45 AM
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Survival Analysis for Medical Imaging Data
Samantha Morrison, Brown University; Jon Steingrimsson, Brown University; Constantine Gatsonis, Brown University
9:55 AM
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Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior
Andrew T Karl, Adsurgo LLC; James Wisnowski, Adsurgo LLC; Lambros Petropoulos, USAA
10:15 AM
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On Deep Learning as a Remedy for the Curse of Dimensionality in Nonparametric Regression
Michael Kohler, Technische Universitaet Darmstadt; Sophie Langer, Technische Universitaet Darmstadt
10:35 AM
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Dynamic Systems Approach to Deep Learning with Different Types of Data Sets and Its Application to Prediction of Alzheimer’s Disease
Momiao Xiong, University of Texas School of Public Health; Helen Engle, University of Texas School of Public Health; Yuanyuan Liu, University of Texas School of Public Health; Zhouxuan Li, University of Texas School of Public Health; Qiyang Ge, University of Texas School of Public Health; Shudi Li, University of Texas School of Public Health; Shan Liu, University of Texas School of Public Health
10:35 AM
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Complementing the Power of Deep Learning with Statistical Model Fusion: Probabilistic Forecasting of Influenza in Dallas County, Texas, USA
Marwah Soliman, University of Texas At Dallas; Yulia Gel, University of Texas at Dallas; Vyacheslav Lyubchich, University of Maryland Center for Environmental Science
10:55 AM
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Generalization Analysis for Mechanism of Deep Learning via Nonparametric Statistics
Masaaki Imaizumi, Institute of Statistical Mathematics
11:35 AM
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Estimating the Amount of Training Data for a Deep Learning Algorithm to Detect Severe Burns
Amy Nussbaum, SpectralMD; Jeffrey Thatcher, SpectralMD; Faliu Yi, SpectralMD; Ron Baxter, SpectralMD; Aadeesh Shringarpure, SpectralMD; Humberto Talavera, SpectralMD; Kevin Plant, SpectralMD
11:35 AM
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GPU Accelerated Deep Learning for Climate and Weather
David Hall, NVIDIA
11:55 AM
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Visual Listening In: Extracting Brand Image Portrayed on Social Media
Liu Liu, University of Colorado Boulder - Leeds School of Business; Daria Dzyabura, New York University Stern School of Business; Natalie Mizik, University of Washington - Foster School of Business
2:05 PM
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Wednesday, 07/31/2019
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High-Dimensional Inference via Adaptive Bayes
Jiapeng Liu, Purdue Unversity; Yixuan Qiu, Carnegie Mellon University; Xiao Wang, Purdue University
8:50 AM
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Learning Grid Cells with Vector Representation of Self-Position and Matrix Representation of Self-Motion
Ying Nian Wu, UCLA
9:25 AM
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Reinforcement Learning as a Solution to Systematic Social Bias in Deep Learning
Kathleen Gatliffe, University of Colorado Denver; Audrey E Hendricks, University of Colorado Denver
10:35 AM
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Deep Model-X Knockoff Generator Through Latent Variables
Ying Liu, Medical College of Wisconsin; Cheng Zheng, University of Wisconsin at Milwakee
10:50 AM
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Using Deep Learning to Build Risk Prediction Models for Time-to-Event Outcomes
Jon Steingrimsson, Brown University; Samantha Morrison, Brown University; Constantine Gatsonis, Brown University
2:05 PM
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Deep Visual Inference: Teaching Computers to See Rather Than Calculate Correlation
Giora Simchoni, vFunction
2:35 PM
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Randomized Linear Algebra and Its Applications in Second-Order Optimization and Deep Learning
Zhewei Yao, UC Berkeley
2:45 PM
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Language Modeling Using SAS
JeeHyun Hwang, SAS Institute Inc.; Xu Yang, SAS Institute Inc.; Haipeng Liu, SAS Institute Inc.
3:05 PM
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Thursday, 08/01/2019
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Statistical and Mathematical Approaches to Cancer Etiology
Cristian Tomasetti, Johsn Hopkins University; Lu Li, Johns Hopkins University
9:05 AM
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Modeling Non-Stationary Multivariate Spatial Data Using Deep Compositional Spatial Models
Andrew Zammit-Mangion, University of Wollongong
9:25 AM
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Transfer Learning in Single Cell Transcriptomics
Nancy Zhang, University of Pennsylvania; Divyansh Agarwal, University of Pennsylvania; Zilu Zhou, University of Pennsylvania; Mo Huang, University of Pennsylvania; Gang Hu, Nankai University; Chengzhong Ye, Tsinghua University; Jingshu Wang, The University of Chicago
9:25 AM
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Deep Learning in Neuroimaging Genetics
Wei Pan, University of Minnesota
10:35 AM
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Deep Learning-Based Histology Image Analysis for Patient Diagnosis and Selection
Xin Huang, AbbVie Inc.; Liuqing Yang, AbbVie; Yan Sun, AbbVie; Mufeng Hu, AbbVie
10:35 AM
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Leveraging Free Text Data for Decision Making in Drug Development
Yan Sun, AbbVie; Jiyeong Jang, University of Illinois at Chicago; Xin Huang, AbbVie Inc.; Hongwei Wang, AbbVie Inc.; Weili He, AbbVie
10:55 AM
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Diagnosis of Diabetic Retinopathy Using Medical Images and Deep Learning Method
Xuanyao He, Eli Lilly and Company
11:15 AM
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Statistical Risk Bounds for Deep Neural Networks
Johannes Schmidt-Hieber, Leiden University
11:25 AM
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Machine Learning Methods for Modeling Animal Movement
Dhanushi Wijeyakulasuriya, Pennsylvania State University; Ephraim Hanks, Pennsylvania State University; Benjamin Shaby, Pennsylvania State University
12:05 PM
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