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Found 5 papers, 0 papers with code
SmartChoices: Augmenting Software with Learned Implementations
We are living in a golden age of machine learning.
Ranking architectures using meta-learning
However, it requires a large amount of computing resources and in order to alleviate this, a performance prediction network has been recently proposed that enables efficient architecture search by forecasting the performance of candidate architectures, instead of relying on actual model training.
Flexible Multi-task Networks by Learning Parameter Allocation
The binary allocation variables are learned jointly with the model parameters by standard back-propagation thanks to the Gumbel-Softmax reparametrization method.
Ranked #1 on
Multi-Task Learning
on OMNIGLOT
Gumbel-Matrix Routing for Flexible Multi-task Learning
We propose the Gumbel-Matrix routing, a novel multi-task routing method based on the Gumbel-Softmax, that is designed to learn fine-grained parameter sharing.
Fast Task-Aware Architecture Inference
At the core of our framework lies a deep value network that can predict the performance of input architectures on a task by utilizing task meta-features and the previous model training experiments performed on related tasks.
