neural networks research group
areas
people
projects
demos
publications
software/data
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.
Publications
None
[Expand to show all 34]
[Minimize]
Semantic Density: Uncertainty Quantification in Semantic Space for Large Language Models
Xin Qiu, Risto Miikkulainen
In
Proceedings of the 38th Conference on Neural Information Processing Systems
, 2024. (also...
2024
NeuroComb: Improving SAT Solving with Graph Neural Networks
Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen
In
Proceedings of the International Conference on Learning Representations
, 2024. (also arX...
2024
Pandemic Resilience: Developing an AI-calibrated Ensemble of Models to Inform Decision Making
GPAI
Technical Report, Global Partnership on Artificial Intelligence, December 2023.
2023
Efficient Activation Function Optimization through Surrogate Modeling
Garrett Bingham and Risto Miikkulainen
In
Proceedings of the 23rd Conference on Neural Information Processing Systems (NeurIPS 2023)
...
2023
Evolutionary Supervised Machine Learning
Risto Miikkulainen
In W. Banzhaf, P. Machado, and M. Zhang, editors,
Handbook of Evolutionary Machine Learning
, ...
2023
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks
Garrett Bingham and Risto Miikkulainen
In
Proceedings of the 37th AAAI Conference on Artificial Intelligence
, 2023. (also arXiv:20...
2023
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model
Xin Qiu and Risto Miikkulainen
In
Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022)
, 2022. (A...
2022
Discovering Parametric Activation Functions
Garrett Bingham and Risto Miikkulainen
Neural Networks
, 148:48-65, 2022.
2022
Regularized Evolutionary Population-Based Training
Jason Liang, Santiago Gonzalez, Hormoz Shahrzad, and Risto Miikkulainen
In
Proceedings of the Genetic and Evolutionary Computation Conference
, 323-331, 2021.
2021
Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization
Santiago Gonzalez and Risto Miikkulainen
In
Proceedings of the Genetic and Evolutionary Computation Conference
, 305-313, 2021.
2021
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
Elliot Meyerson and Risto Miikkulainen
To Appear In
International Conference on Learning Representations
, 2021.
2021
Improving Neural Network Learning Through Dual Variable Learning Rates
Elizabeth Liner, Risto Miikkulainen
In
Proceedings of the International Joint Conference on Neural Networks
, 2021.
2021
Effective Regularization Through Loss-Function Metalearning
Santiago Gonzalez and Risto Miikkulainen
In
arXiv:2010.00788
, 2021.
2021
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization
Santiago Gonzalez and Risto Miikkulainen
In
Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC)
, 1-8, July 2020.
2020
Evolutionary Optimization of Deep Learning Activation Functions
Garrett Bingham, William Macke, and Risto Miikkulainen
In
Genetic and Evolutionary Computation Conference (GECCO '20)
, 289-296, Cancun, Mexico, 20...
2020
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
Xin Qiu, Elliot Meyerson, Risto Miikkulainen
In
International Conference on Learning Representations
, 2020.
2020
From Nodes to Networks: Evolving Recurrent Neural Networks
Aditya Rawal, Risto Miikkulainen
In H. Iba and N. Noman, editors,
Deep Neural Evolution: Deep Learning with Evolutionary Computati...
2020
Improving Deep Learning Through Loss-Function Evolution
Santiago Gonzalez
PhD Thesis, Department of Computer Science, The University of Texas at Austin, 2020.
2020
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
Elliot Meyerson and Risto Miikkulainen
arxiv:2010.02354
, October 2020.
2020
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 ...
2019
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back
Elliot Meyerson, Risto Miikkulainen
In
Proceedings of the 35th International Conference on Machine Learning
, 739-748, 2018.
2018
Discovering Gated Recurrent Neural Network Architectures
Aditya Rawal
PhD Thesis, Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, 201...
2018
Learning Useful Features For Poker
Arjun Nagineni
Technical Report, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 20...
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
AI Magazine
, 35:64-75, 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
Parsing Embedded Clauses with Distributed Neural Networks
Risto Miikkulainen and Dennis Bijwaard
In
Proceedings of the Twelfth National Conference on Artificial Intelligence
, 858-864, Januar...
1994
People
None
[Expand to show all 15]
[Minimize]
Garrett Bingham
Ph.D. Alumni
bingham [at] cs utexas edu
Eliana Feasley
Former Ph.D. Student
elie [at] cs utexas edu
Peggy Fidelman
Former Ph.D. Student
peggyf [at] cs utexas edu
Olivier Francon
Collaborator
olivier francon [at] cognizant com
Kim Houck
Ph.D. Alumni
houck [at] cs utexas edu
Alan J. Lockett
Ph.D. Alumni
alan lockett [at] gmail com
Marlan McInnes-Taylor
Masters Student
marlan [at] cs utexas edu
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Arjun Nagineni
Undergraduate Alumni
arjun nagineni [at] utexas edu
Xin Qiu
Collaborator
xin qiu [at] cognizant com
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
Austin Waters
Ph.D. Alumni
austin [at] cs utexas edu
Projects
None
Intrusion Detection
1998 - 1998
Data Rectification for Process Control
1992 - 1992
Demos
None
Egalitarian Social Learning (ESL) in Robot Foraging
Wesley Tansey
2012
Software/Data
None
ESL
This is the C# source code for the experiments with Egalitarian Social Learning (ESL) in a robot foraging domain. The re...
2012