ERC Consolidator Grant · 2021
DeepLearning 2.0: Meta-Learning Qualitatively New Components
Deep learning has revolutionized many fields, such as computer vision, speech recognition, natural language processing, and reinforcement learning. This success is based on replacing domain-specific hand-crafted features 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.…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.