Stephen D. Scott

According to our database, Stephen D. Scott authored at least 51 papers between 1995 and 2022.

Collaborative distances:

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

2022

Self-Supervised Learning in the Twilight of Noisy Real-World Datasets.

Atharva Tendle, Andrew Little, Stephen D. Scott, Mohammad Rashedul Hasan

Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

Bayesian Deep Structured Semantic Model for Sub-Linear-Time Information Retrieval.

Yuji Mo, Stephen D. Scott

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks.

Zhongyuan Zhao, Mehmet Can Vuran, Fujuan Guo, Stephen D. Scott

IEEE J. Sel. Areas Commun., 2021

Maize Tassel Detection From UAV Imagery Using Deep Learning.

Aziza Alzadjali, Mohammed H. Alali, Arun Narenthiran Veeranampalayam Sivakumar, Jitender S. Deogun, Stephen Scott, James C. Schnable, Yeyin Shi

Frontiers Robotics AI, 2021

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

Haluk Dogan, Zeynep Hakguder, Roland Madadjim, Stephen Scott, Massimiliano Pierobon, Juan Cui

Briefings Bioinform., 2021

2020

Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery.

Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Remote. Sens., 2020

2019

Formal Language Constraints for Markov Decision Processes.

Eleanor Quint, Dong Xu, Haluk Dogan, Zeynep Hakguder, Stephen Scott, Matthew B. Dwyer

CoRR, 2019

Elucidation of MicroRNA-Gene Regulation in Human Cancer with Integrative Network Models.

Haluk Dogan, Zeynep Hakguder, Stephen Scott, Juan Cui

Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

2018

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks.

Zhongyuan Zhao, Mehmet C. Vuran, Fujuan Guo, Stephen D. Scott

CoRR, 2018

2017

Multiple-Instance Learning.

Soumya Ray, Stephen Scott, Hendrik Blockeel

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Multi-Instance Learning.

Soumya Ray, Stephen Scott, Hendrik Blockeel

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

2016

Constrained Group Testing to Predict Binding Response of Candidate Compounds.

Paul Quint, Stephen D. Scott, N. V. Vinodchandran, Bradley Worley

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Learning Hierarchically Decomposable Concepts with Active Over-Labeling.

Yuji Mo, Stephen D. Scott, Doug Downey

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Computing triangle and open-wedge heavy-hitters in large networks.

Aduri Pavan, Paul Quint, Stephen D. Scott, N. V. Vinodchandran, J. Smith

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015

Genetic Algorithm Classifier System for Semi-Supervised Learning.

Lee Dee Miller, Leen-Kiat Soh, Stephen Scott

Comput. Intell., 2015

2014

Yeast pheromone pathway modeling using Petri nets.

Abhishek Majumdar, Stephen D. Scott, Jitender S. Deogun, Steven Harris

BMC Bioinform., 2014

2013

New algorithms for budgeted learning.

Kun Deng, Yaling Zheng, Chris Bourke, Stephen Scott, Julie Masciale

Mach. Learn., 2013

Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers.

Daniel J. Geschwender, Shant Karakashian, Robert J. Woodward, Berthe Y. Choueiry, Stephen D. Scott

Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2010

Multi-Instance Learning.

Soumya Ray, Stephen Scott, Hendrik Blockeel

Proceedings of the Encyclopedia of Machine Learning, 2010

Active Learning from Multiple Noisy Labelers with Varied Costs.

Yaling Zheng, Stephen Scott, Kun Deng

Proceedings of the ICDM 2010, 2010

2009

Renaissance computing: an initiative for promoting student participation in computing.

Leen-Kiat Soh, Ashok Samal, Stephen D. Scott, Stephen Ramsay, Etsuko Moriyama, George Meyer, Brian Moore, William G. Thomas, Duane F. Shell

Proceedings of the 40th SIGCSE Technical Symposium on Computer Science Education, 2009

2008

Kernels for Generalized Multiple-Instance Learning.

Qingping Tao, Stephen D. Scott, N. V. Vinodchandran, Thomas Takeo Osugi, Brandon Mueller

IEEE Trans. Pattern Anal. Mach. Intell., 2008

Improved MCMC sampling methods for estimating weighted sums in Winnow with application to DNF learning.

Qingping Tao, Stephen D. Scott

Mach. Learn., 2008

On reoptimizing multi-class classifiers.

Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran

Mach. Learn., 2008

2007

Bandit-Based Algorithms for Budgeted Learning.

Kun Deng, Chris Bourke, Stephen Scott, Julie Sunderman, Yaling Zheng

Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

2006

Active Learning to Maximize Area Under the ROC Curve.

Matt Culver, Kun Deng, Stephen Scott

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

2005

On Generalized Multiple-instance Learning.

Stephen Scott, Jun Zhang, Joshua Brown

Int. J. Comput. Intell. Appl., 2005

New kernels for protein structural motif discovery and function classification.

Chang Wang, Stephen D. Scott

Proceedings of the Machine Learning, 2005

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning.

Thomas Takeo Osugi, Kun Deng, Stephen Scott

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

2004

On approximating weighted sums with exponentially many terms.

Deepak Chawla, Lin Li, Stephen Scott

J. Comput. Syst. Sci., 2004

New Techniques for Generation and Analysis of Evolutionary Trees.

Chang Wang, Stephen D. Scott, Qingping Tao, Dmitri E. Fomenko, Vadim N. Gladyshev

Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Scienes, 2004

An Extended Kernel for Generalized Multiple-Instance Learning.

Qingping Tao, Stephen Scott, N. V. Vinodchandran, Thomas Takeo Osugi, Brandon Mueller

Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), 2004

Agnostic learning of general geometric patterns and multi-instance learning in R^d.

Stephen D. Scott

Proceedings of the 2004 International Conference on Machine Learning and Applications, 2004

LASSO: a learning architecture for semantic web ontologies.

Christopher N. Hammack, Stephen D. Scott

Proceedings of the 2004 International Conference on Machine Learning and Applications, 2004

Creating an SVM to play strong poker.

Todd Blank, Leen-Kiat Soh, Stephen Scott

Proceedings of the 2004 International Conference on Machine Learning and Applications, 2004

EMPRR: a high-dimensional EM-based peicewise regression algorithm.

Manimozhiyan Arumugam, Stephen D. Scott

Proceedings of the 2004 International Conference on Machine Learning and Applications, 2004

SVM-based generalized multiple-instance learning via approximate box counting.

Qingping Tao, Stephen Donald Scott, N. V. Vinodchandran, Thomas Takeo Osugi

Proceedings of the Machine Learning, 2004

A Faster Algorithm for Generalized Multiple-Instance Learning.

Qingping Tao, Stephen D. Scott

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, 2004

2003

Learning from examples with unspecified attribute values.

Sally A. Goldman, Stephen Kwek, Stephen D. Scott

Inf. Comput., 2003

Multiple-Instance Learning of Real-Valued Geometric Patterns.

Sally A. Goldman, Stephen D. Scott

Ann. Math. Artif. Intell., 2003

A novel fiber delay line buffering architecture for optical packet switching.

Lin Li, Stephen D. Scott, Jitender S. Deogun

Proceedings of the Global Telecommunications Conference, 2003

2001

Agnostic Learning of Geometric Patterns.

Sally A. Goldman, Stephen Kwek, Stephen D. Scott

J. Comput. Syst. Sci., 2001

On-line analysis of the TCP acknowledgment delay problem.

Daniel R. Dooly, Sally A. Goldman, Stephen D. Scott

J. ACM, 2001

Efficiently Approximating Weighted Sums with Exponentially Many Terms.

Deepak Chawla, Lin Li, Stephen Scott

Proceedings of the Computational Learning Theory, 2001

1999

A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns.

Sally A. Goldman, Stephen D. Scott

Mach. Learn., 1999

1998

TCP Dynamic Acknowledgment Delay: Theory and Practice (Extended Abstract).

Daniel R. Dooly, Sally A. Goldman, Stephen D. Scott

Proceedings of the Thirtieth Annual ACM Symposium on the Theory of Computing, 1998

1997

Agnostic Learning of Geometric Patterns (Extended Abstract).

Sally A. Goldman, Stephen Kwek, Stephen D. Scott

Proceedings of the Tenth Annual Conference on Computational Learning Theory, 1997

Learning from Examples with Unspecified Attribute Values (Extended Abstract).

Sally A. Goldman, Stephen Kwek, Stephen D. Scott

Proceedings of the Tenth Annual Conference on Computational Learning Theory, 1997

1996

PAC Learning of One-Dimensional Patterns.

Paul W. Goldberg, Sally A. Goldman, Stephen D. Scott

Mach. Learn., 1996

A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns.

Sally A. Goldman, Stephen D. Scott

Proceedings of the Machine Learning, 1996

1995

HGA: A Hardware-Based Genetic Algorithm.

Stephen D. Scott, Ashok Samal, Sharad C. Seth

Proceedings of the Third International ACM Symposium on Field-Programmable Gate Arrays, 1995