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Found 4 papers, 3 papers with code
Continuous-Time Deep Glioma Growth Models
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.
Sample-Efficient Automated Deep Reinforcement Learning
Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters.
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control
Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures.
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