124 citations · 242 across the 5 of their papers we have counts for
5 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…
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman +2
This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in orde…
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…
A Proposed Infrastructure for Adding Online Interaction to Any Evolutionary Domain
Paul Szerlip, Kenneth O. Stanley
To address the difficulty of creating online collaborative evolutionary systems, this paper presents a new prototype library called Worldwide Infrastructure for Neuroevolution (WIN…
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation
Paul A. Szerlip, Gregory Morse, Justin K. Pugh +1
Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called di…