Risto Miikkulainen

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The University of Texas at Austin ∙ Evolv Technologies ∙ cognizant

Professor of Computer Science at the University of Texas at Austin and VP of Research at Sentient Technologies.

The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings

This paper frames a general prediction system as an observer traveling a...
10/05/2020 ∙ by Elliot Meyerson, et al. ∙ 0 ∙ share

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Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model

As neural network classifiers are deployed in real-world applications, i...
10/05/2020 ∙ by Xin Qiu, et al. ∙ 0 ∙ share

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Effective Regularization Through Loss-Function Metalearning

Loss-function metalearning can be used to discover novel, customized los...
10/02/2020 ∙ by Santiago Gonzalez, et al. ∙ 0 ∙ share

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Creative AI Through Evolutionary Computation: Principles and Examples

The main power of artificial intelligence is not in modeling what we alr...
08/04/2020 ∙ by Risto Miikkulainen, et al. ∙ 0 ∙ share

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Generalization of Agent Behavior through Explicit Representation of Context

In order to deploy autonomous agents in digital interactive environments...
06/18/2020 ∙ by Cem C. Tutum, et al. ∙ 0 ∙ share

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Discovering Parametric Activation Functions

Recent studies have shown that the choice of activation function can sig...
06/05/2020 ∙ by Garrett Bingham, et al. ∙ 0 ∙ share

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From Prediction to Prescription: AI-Based Optimization of Non-Pharmaceutical Interventions for the COVID-19 Pandemic

Several models have been developed to predict how the COVID-19 pandemic ...
05/28/2020 ∙ by Risto Miikkulainen, et al. ∙ 24 ∙ share

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Evolutionary Optimization of Deep Learning Activation Functions

The choice of activation function can have a large effect on the perform...
02/17/2020 ∙ by Garrett Bingham, et al. ∙ 0 ∙ share

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Adapting to Unseen Environments through Explicit Representation of Context

In order to deploy autonomous agents to domains such as autonomous drivi...
02/13/2020 ∙ by Cem C. Tutum, et al. ∙ 0 ∙ share

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Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription

There is now significant historical data available on decision making in...
02/13/2020 ∙ by Olivier Francon, et al. ∙ 9 ∙ share

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Population-Based Training for Loss Function Optimization

Metalearning of deep neural network (DNN) architectures and hyperparamet...
02/11/2020 ∙ by Jason Liang, et al. ∙ 0 ∙ share

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Improving Neural Network Learning Through Dual Variable Learning Rates

This paper introduces and evaluates a novel training method for neural n...
02/09/2020 ∙ by Elizabeth Liner, et al. ∙ 0 ∙ share

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MDEA: Malware Detection with Evolutionary Adversarial Learning

Malware detection have used machine learning to detect malware in progra...
02/09/2020 ∙ by Xiruo Wang, et al. ∙ 0 ∙ share

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Evolving Loss Functions With Multivariate Taylor Polynomial Parameterizations

Loss function optimization for neural networks has recently emerged as a...
01/31/2020 ∙ by Santiago Gonzalez, et al. ∙ 0 ∙ share

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Enhanced Optimization with Composite Objectives and Novelty Pulsation

An important benefit of multi-objective search is that it maintains a di...
06/07/2019 ∙ by Hormoz Shahrzad, et al. ∙ 2 ∙ share

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Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel

Neural Networks (NNs) have been extensively used for a wide spectrum of ...
06/03/2019 ∙ by Xin Qiu, et al. ∙ 0 ∙ share

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Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains

As deep learning applications continue to become more diverse, an intere...
05/31/2019 ∙ by Elliot Meyerson, et al. ∙ 0 ∙ share

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Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential

Artificial Intelligence (AI) technology is rapidly changing many areas o...
05/30/2019 ∙ by Risto Miikkulainen, et al. ∙ 8 ∙ share

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Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization

As the complexity of neural network models has grown, it has become incr...
05/27/2019 ∙ by Santiago Gonzalez, et al. ∙ 0 ∙ share

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Functional Generative Design of Mechanisms with Recurrent Neural Networks and Novelty Search

Consumer-grade 3D printers have made it easier to fabricate aesthetic ob...
03/25/2019 ∙ by Cameron R. Wolfe, et al. ∙ 0 ∙ share

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Evolutionary Neural AutoML for Deep Learning

Deep neural networks (DNNs) have produced state-of-the-art results in ma...
02/18/2019 ∙ by Jason Liang, et al. ∙ 26 ∙ share

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Creative AI Through Evolutionary Computation

In the last decade or so we have seen tremendous progress in Artificial ...
01/12/2019 ∙ by Risto Miikkulainen, et al. ∙ 0 ∙ share

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Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play

Training robots with physical bodies requires developing new methods and...
11/30/2018 ∙ by Reza Mahjourian, et al. ∙ 0 ∙ share

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A Comparison of the Taguchi Method and Evolutionary Optimization in Multivariate Testing

Multivariate testing has recently emerged as a promising technique in we...
08/25/2018 ∙ by Jingbo Jiang, et al. ∙ 0 ∙ share

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Functional Generative Design: An Evolutionary Approach to 3D-Printing

Consumer-grade printers are widely available, but their ability to print...
04/19/2018 ∙ by Cem C. Tutum, et al. ∙ 6 ∙ share

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From Nodes to Networks: Evolving Recurrent Neural Networks

Gated recurrent networks such as those composed of Long Short-Term Memor...
03/12/2018 ∙ by Aditya Rawal, et al. ∙ 0 ∙ share

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Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back

Deep multitask learning boosts performance by sharing learned structure ...
03/11/2018 ∙ by Elliot Meyerson, et al. ∙ 0 ∙ share

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Evolutionary Architecture Search For Deep Multitask Networks

Multitask learning, i.e. learning several tasks at once with the same ne...
03/10/2018 ∙ by Jason Liang, et al. ∙ 0 ∙ share

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Enhanced Optimization with Composite Objectives and Novelty Selection

An important benefit of multi-objective search is that it maintains a di...
03/10/2018 ∙ by Hormoz Shahrzad, et al. ∙ 0 ∙ share

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Enhancing Evolutionary Optimization in Uncertain Environments by Allocating Evaluations via Multi-armed Bandit Algorithms

Optimization problems with uncertain fitness functions are common in the...
03/10/2018 ∙ by Xin Qiu, et al. ∙ 0 ∙ share

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The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Biological evolution provides a creative fount of complex and subtle ada...
03/09/2018 ∙ by Joel Lehman, et al. ∙ 0 ∙ share

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Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering

Existing deep multitask learning (MTL) approaches align layers shared be...
10/31/2017 ∙ by Elliot Meyerson, et al. ∙ 0 ∙ share

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Discovering Evolutionary Stepping Stones through Behavior Domination

Behavior domination is proposed as a tool for understanding and harnessi...
04/18/2017 ∙ by Elliot Meyerson, et al. ∙ 0 ∙ share

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Conversion Rate Optimization through Evolutionary Computation

Conversion optimization means designing a web interface so that as many ...
03/01/2017 ∙ by Risto Miikkulainen, et al. ∙ 0 ∙ share

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Evolving Deep Neural Networks

The success of deep learning depends on finding an architecture to fit t...
03/01/2017 ∙ by Risto Miikkulainen, et al. ∙ 0 ∙ share

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Reuse of Neural Modules for General Video Game Playing

A general approach to knowledge transfer is introduced in which an agent...
12/04/2015 ∙ by Alexander Braylan, et al. ∙ 0 ∙ share

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Increasing Behavioral Complexity for Evolved Virtual Creatures with the ESP Method

Since their introduction in 1994 (Sims), evolved virtual creatures (EVCs...
10/27/2015 ∙ by Dan Lessin, et al. ∙ 0 ∙ share

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Risto Miikkulainen

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