Supervised Learning
In supervised learning the desired outputs are known for each input, and the task is to learn a mapping between them that generalizes well to new inputs. Our research in this area includes various applications of neural networks and related methods.
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Evolving Deep Neural Networks Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju... In Robert Kozma, Cesare Alippi, Yoonsuck Choe, and Francesco Carlo Morabito, editors, Artificial ... 2018

Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering Elliot Meyerson and Risto Miikkulainen In Proceedings of the Sixth International Conference on Learning Representations (ICLR), Vanc... 2018

Efficient Sampling for Design Optimization of an SLS Product Nancy Xu, Cem C. Tutum In Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium, 12, Aus... 2017

Surrogate-based Evolutionary Optimization for Friction Stir Welding Cem C Tutum, Shaayaan Sayed and Risto Miikkulainen In Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2016), 8 pages, Van... 2016

GRADE: Machine Learning Support for Graduate Admissions Austin Waters, Risto Miikkulainen To Appear In AI Magazine, 2014. 2014

Infinite-Word Topic Models for Digital Media Austin Waters PhD Thesis, Department of Computer Science, The University of Texas at Austin, 2014. 2014

GRADE: Machine Learning Support for Graduate Admissions Austin Waters, Risto Miikkulainen In Proceedings of the 25th Conference on Innovative Applications of Artificial Intelligence, ... 2013

Accelerating Evolution via Egalitarian Social Learning Wesley Tansey, Eliana Feasley, and Risto Miikkulainen In Proceedings of the 14th Annual Genetic and Evolutionary Computation Conference (GECCO 2012) 2012

Temporal Convolution Machines for Sequence Learning Alan J Lockett and Risto Miikkulainen Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin, 2009. 2009

Detecting Motion in the Environment with a Moving Quadruped Robot Peggy Fidelman, Thayne Coffman and Risto Miikkulainen In Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi, editors, Ro... 2007

Learning Concept Drift with a Committee of Decision Trees Kenneth O. Stanley Technical Report AI03-302, Department of Computer Sciences, The University of Texas at Austin, 2003. 2003

Eliana Feasley Former Ph.D. Student elie [at] cs utexas edu
Peggy Fidelman Former Ph.D. Student
Alan J. Lockett Ph.D. Alumni alan lockett [at] gmail com
Risto Miikkulainen Faculty risto [at] cs utexas edu
Vito Ruiz Masters Alumni
Jake Ryan Undergraduate Alumni
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
Wesley Tansey Former Collaborator tansey [at] cs utexas edu
Cem C Tutum Research Scientist tutum [at] cs utexas edu
Austin Waters Ph.D. Alumni austin [at] cs utexas edu
ESL This is the C# source code for the experiments with Egalitarian Social Learning (ESL) in a robot foraging domain. The re... 2012