b'Jeff Clune'

research

∙ 07/05/2023

First-Explore, then Exploit: Meta-Learning Intelligent Exploration

Standard reinforcement learning (RL) agents never intelligently explore ...

0 Ben Norman, et al. ∙

research

∙ 06/02/2023

OMNI: Open-endedness via Models of human Notions of Interestingness

Open-ended algorithms aim to learn new, interesting behaviors forever. T...

3 Jenny Zhang, et al. ∙

research

∙ 06/01/2023

Thought Cloning: Learning to Think while Acting by Imitating Human Thinking

Language is often considered a key aspect of human thinking, providing u...

1 Shengran Hu, et al. ∙

research

∙ 06/23/2022

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

Pretraining on noisy, internet-scale datasets has been heavily studied a...

0 Bowen Baker, et al. ∙

research

∙ 08/26/2021

Continual learning under domain transfer with sparse synaptic bursting

Existing machines are functionally specific tools that were made for eas...

3 Shawn L. Beaulieu, et al. ∙

research

∙ 06/28/2021

Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft

An important challenge in reinforcement learning is training agents that...

0 Ingmar Kanitscheider, et al. ∙

research

∙ 06/12/2020

Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity

Artificial life originated and has long studied the topic of open-ended ...

0 Adrien Ecoffet, et al. ∙

research

∙ 05/27/2020

Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search

Neural Architecture Search (NAS) explores a large space of architectural...

18 Aditya Rawal, et al. ∙

research

∙ 04/27/2020

First return then explore

The promise of reinforcement learning is to solve complex sequential dec...

4 Adrien Ecoffet, et al. ∙

research

∙ 03/25/2020

Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

Recent advances in machine learning are consistently enabled by increasi...

3 Jiale Zhi, et al. ∙

research

∙ 03/19/2020

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Creating open-ended algorithms, which generate their own never-ending st...

0 Rui Wang, et al. ∙

research

∙ 03/03/2020

Scaling MAP-Elites to Deep Neuroevolution

Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, ha...

8 Cédric Colas, et al. ∙

research

∙ 02/24/2020

Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity

The impressive lifelong learning in animal brains is primarily enabled b...

25 Thomas Miconi, et al. ∙

research

∙ 02/21/2020

Learning to Continually Learn

Continual lifelong learning requires an agent or model to learn many seq...

43 Shawn Beaulieu, et al. ∙

research

∙ 12/17/2019

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

This paper investigates the intriguing question of whether we can create...

36 Felipe Petroski Such, et al. ∙

research

∙ 10/22/2019

A deep active learning system for species identification and counting in camera trap images

Biodiversity conservation depends on accurate, up-to-date information ab...

8 Mohammad Sadegh Norouzzadeh, et al. ∙

research

∙ 07/13/2019

Evolvability ES: Scalable and Direct Optimization of Evolvability

Designing evolutionary algorithms capable of uncovering highly evolvable...

11 Alexander Gajewski, et al. ∙

research

∙ 05/27/2019

AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

Perhaps the most ambitious scientific quest in human history is the crea...

0 Jeff Clune, et al. ∙

research

∙ 04/18/2019

Understanding Neural Networks via Feature Visualization: A survey

A neuroscience method to understanding the brain is to find and study th...

38 Anh Nguyen, et al. ∙

research

∙ 01/30/2019

Go-Explore: a New Approach for Hard-Exploration Problems

A grand challenge in reinforcement learning is intelligent exploration, ...

34 Adrien Ecoffet, et al. ∙

research

∙ 01/07/2019

Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions

While the history of machine learning so far encompasses a series of pro...

8 Rui Wang, et al. ∙

research

∙ 12/27/2018

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

Deep learning provides a powerful tool for machine perception when the o...

10 Rowan McAllister, et al. ∙

research

∙ 12/17/2018

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

Much human and computational effort has aimed to improve how deep reinfo...

6 Felipe Petroski Such, et al. ∙

research

∙ 07/09/2018

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm

An important challenge in reinforcement learning, including evolutionary...

2 Joost Huizinga, et al. ∙

research

∙ 06/01/2018

Deep Curiosity Search: Intra-Life Exploration Improves Performance on Challenging Deep Reinforcement Learning Problems

Traditional exploration methods in RL require agents to perform random a...

0 Christopher Stanton, et al. ∙

research

∙ 05/03/2018

VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

Recent advances in deep neuroevolution have demonstrated that evolutiona...

4 Rui Wang, et al. ∙

research

∙ 04/06/2018

Differentiable plasticity: training plastic neural networks with backpropagation

How can we build agents that keep learning from experience, quickly and ...

0 Thomas Miconi, et al. ∙

research

∙ 03/09/2018

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...

0 Joel Lehman, et al. ∙

research

∙ 12/18/2017

ES Is More Than Just a Traditional Finite-Difference Approximator

An evolution strategy (ES) variant recently attracted significant attent...

0 Joel Lehman, et al. ∙

research

∙ 12/18/2017

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Deep artificial neural networks (DNNs) are typically trained via gradien...

0 Felipe Petroski Such, et al. ∙

research

∙ 12/18/2017

On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent

Because stochastic gradient descent (SGD) has shown promise optimizing n...

0 Xingwen Zhang, et al. ∙

research

∙ 12/18/2017

Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients

While neuroevolution (evolving neural networks) has a successful track r...

0 Joel Lehman, et al. ∙

research

∙ 12/18/2017

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

Evolution strategies (ES) are a family of black-box optimization algorit...

0 Edoardo Conti, et al. ∙

research

∙ 05/20/2017

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

A long-term goal of AI is to produce agents that can learn a diversity o...

0 Roby Velez, et al. ∙

research

∙ 04/17/2017

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Natural evolution has produced a tremendous diversity of functional orga...

0 Joost Huizinga, et al. ∙

research

∙ 03/16/2017

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Having accurate, detailed, and up-to-date information about the location...

0 Mohammed Sadegh Norouzzadeh, et al. ∙

research

∙ 11/30/2016

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

Generating high-resolution, photo-realistic images has been a long-stand...

0 Anh Nguyen, et al. ∙

research

∙ 05/30/2016

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

Deep neural networks (DNNs) have demonstrated state-of-the-art results o...

0 Anh Nguyen, et al. ∙

research

∙ 02/11/2016

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks

We can better understand deep neural networks by identifying which featu...

0 Anh Nguyen, et al. ∙

research

∙ 11/24/2015

Convergent Learning: Do different neural networks learn the same representations?

Recent success in training deep neural networks have prompted active inv...

0 Yixuan Li, et al. ∙

research

∙ 06/22/2015

Understanding Neural Networks Through Deep Visualization

Recent years have produced great advances in training large, deep neural...

0 Jason Yosinski, et al. ∙

research

∙ 05/23/2015

The evolutionary origins of hierarchy

Hierarchical organization -- the recursive composition of sub-modules --...

0 Henok Mengistu, et al. ∙

research

∙ 04/20/2015

Illuminating search spaces by mapping elites

Many fields use search algorithms, which automatically explore a search ...

0 Jean-Baptiste Mouret, et al. ∙

research

∙ 12/05/2014

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Deep neural networks (DNNs) have recently been achieving state-of-the-ar...

0 Anh Nguyen, et al. ∙

research

∙ 11/06/2014

How transferable are features in deep neural networks?

Many deep neural networks trained on natural images exhibit a curious ph...

0 Jason Yosinski, et al. ∙

research

∙ 07/13/2014

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously...

0 Antoine Cully, et al. ∙

research

∙ 04/17/2013

Hands-free Evolution of 3D-printable Objects via Eye Tracking

Interactive evolution has shown the potential to create amazing and comp...

0 Nick Cheney, et al. ∙

research

∙ 07/11/2012

The evolutionary origins of modularity

A central biological question is how natural organisms are so evolvable ...

0 Jeff Clune, et al. ∙

Jeff Clune

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