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.

Evolving GAN Formulations for Higher Quality Image Synthesis

Generative Adversarial Networks (GANs) have extended deep learning to co...
02/17/2021 ∙ by Santiago Gonzalez, et al. ∙ 0 ∙ share

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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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