3.5k citations · 4.7k across the 7 of their papers we have counts for
7 papers
Learning to Continually Learn
Shawn Beaulieu, Lapo Frati, Thomas Miconi +4
Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work ha…
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
Rui Wang, Joel Lehman, Jeff Clune +1
While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is wheth…
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
Rowan McAllister, Gregory Kahn, Jeff Clune +1
Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to thei…
Resolving the paradox of evolvability with learning theory: How evolution learns to improve evolvability on rugged fitness landscapes
Loizos Kounios, Jeff Clune, Kostas Kouvaris +4
It has been hypothesized that one of the main reasons evolution has been able to produce such impressive adaptations is because it has improved its own ability to evolve -- "the ev…
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Nguyen, Jason Yosinski, Jeff Clune
Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given…
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio +1
Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such fi…