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Found 3 papers, 1 papers with code
Fine-Grained Neural Architecture Search
We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even generate compositional feature maps using several different base operations.
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Catastrophic forgetting is a critical challenge in training deep neural networks.
Learning to Forget for Meta-Learning
Model-agnostic meta-learning (MAML) tackles the problem by formulating prior knowledge as a common initialization across tasks, which is then used to quickly adapt to unseen tasks.
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