415 citations · 760 across the 9 of their papers we have counts for
18 papers
Towards Consistent Predictive Confidence through Fitted Ensembles
Navid Kardan, Ankit Sharma, Kenneth O. Stanley
Deep neural networks are behind many of the recent successes in machine learning applications. However, these models can produce overconfident decisions while encountering out-of-d…
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
Aditya Rawal, Joel Lehman, Felipe Petroski Such +2
Neural Architecture Search (NAS) explores a large space of architectural motifs -- a compute-intensive process that often involves ground-truth evaluation of each motif by instanti…
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang, Joel Lehman, Aditya Rawal +4
Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate pr…
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods
Jiale Zhi, Rui Wang, Jeff Clune +1
Recent advances in machine learning are consistently enabled by increasing amounts of computation. Reinforcement learning (RL) and population-based methods in particular pose uniqu…
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…
Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures
Sebastian Risi, Kenneth O. Stanley
Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models requi…