SOURCE-LINKED INTELLIGENCE

DeepLearning 2.0: Meta-Learning Qualitatively New Components

CORDIS · observation · Publication date unknown

es with features that are learned for the particular task at hand. The logical step to take deep learning to the next level is to also (meta-)learn other hand-crafted elements of the deep learning pipeline. We therefore propose to develop meta-level learning methods for the creation of novel customized deep learning pipelines, by means of: 1. Hierarchical neural architecture searchfor learning qualitatively new architectures and architectural building blocks from scratch; 2. Learning of optimizers and hyperparameter adaptation policies that adapt totheir context in order to converge faster and more robustly; 3. Learning the data to train on, to remove the need for large sets of labelled data; and 4. Bootstrapping from prior design efforts to increase efficiency and make an integrative design of architectures, optimizers, hyperparameter adaptation policies, and pretraining tasks feasible in practice. These advances will allow the next generation of deep learning pipelines to achieve higher accuracy, lower training time, and improved ease-of-use (

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

2000000

unit

EUR