415 citations · 725 across the 47 of their papers we have counts for
14 papers · 1 filter
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
Elliot Meyerson, Risto Miikkulainen
This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is…
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model
Xin Qiu, Risto Miikkulainen
As neural network classifiers are deployed in real-world applications, it is crucial that their failures can be detected reliably. One practical solution is to assign confidence sc…
Creative AI Through Evolutionary Computation: Principles and Examples
Risto Miikkulainen
The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional…
Characterizing the Effect of Sentence Context on Word Meanings: Mapping Brain to Behavior
N. Aguirre-Celis, R. Miikkulainen
Semantic feature models have become a popular tool for prediction and interpretation of fMRI data. In particular, prior work has shown that differences in the fMRI patterns in sent…
Generalization of Agent Behavior through Explicit Representation of Context
Cem C Tutum, Suhaib Abdulquddos, Risto Miikkulainen
In order to deploy autonomous agents in digital interactive environments, they must be able to act robustly in unseen situations. The standard machine learning approach is to inclu…
Discovering Parametric Activation Functions
Garrett Bingham, Risto Miikkulainen
Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks. However, the benefits of novel activation funct…
From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic
Risto Miikkulainen, Olivier Francon, Elliot Meyerson +3
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancin…
Evolutionary Optimization of Deep Learning Activation Functions
Garrett Bingham, William Macke, Risto Miikkulainen
The choice of activation function can have a large effect on the performance of a neural network. While there have been some attempts to hand-engineer novel activation functions, t…
Adapting to Unseen Environments through Explicit Representation of Context
Cem C. Tutum, Risto Miikkulainen
In order to deploy autonomous agents to domains such as autonomous driving, infrastructure management, health care, and finance, they must be able to adapt safely to unseen situati…
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Olivier Francon, Santiago Gonzalez, Babak Hodjat +4
There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes…
Regularized Evolutionary Population-Based Training
Jason Liang, Santiago Gonzalez, Hormoz Shahrzad +1
Metalearning of deep neural network (DNN) architectures and hyperparameters has become an increasingly important area of research. At the same time, network regularization has been…
Improving Neural Network Learning Through Dual Variable Learning Rates
Elizabeth Liner, Risto Miikkulainen
This paper introduces and evaluates a novel training method for neural networks: Dual Variable Learning Rates (DVLR). Building on insights from behavioral psychology, the dual lear…
MDEA: Malware Detection with Evolutionary Adversarial Learning
Xiruo Wang, Risto Miikkulainen
Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign o…
Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization
Santiago Gonzalez, Risto Miikkulainen
Metalearning of deep neural network (DNN) architectures and hyperparameters has become an increasingly important area of research. Loss functions are a type of metaknowledge that i…