Search Results for author:
Found 7 papers, 2 papers with code
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis
An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation.
Curiosity creates Diversity in Policy Search
In this work, we use a recently proposed definition of intrinsic motivation, Curiosity, in an evolutionary policy search method.
Equivariance-aware Architectural Optimization of Neural Networks
Incorporating equivariance to symmetry groups as a constraint during neural network training can improve performance and generalization for tasks exhibiting those symmetries, but such symmetries are often not perfectly nor explicitly present.
On Neural Consolidation for Transfer in Reinforcement Learning
Although transfer learning is considered to be a milestone in deep reinforcement learning, the mechanisms behind it are still poorly understood.
Neural Distillation as a State Representation Bottleneck in Reinforcement Learning
Learning a good state representation is a critical skill when dealing with multiple tasks in Reinforcement Learning as it allows for transfer and better generalization between tasks.
When, where, and how to add new neurons to ANNs
Neurogenesis in ANNs is an understudied and difficult problem, even compared to other forms of structural learning like pruning.
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS
Differentiable Architecture Search (DARTS) is a recent neural architecture search (NAS) method based on a differentiable relaxation.
Ranked #10 on
Neural Architecture Search
on CIFAR-100
