2 papers
cs.LG2021
In-Loop Meta-Learning with Gradient-Alignment Reward
Samuel Müller, André Biedenkapp, Frank Hutter
At the heart of the standard deep learning training loop is a greedy gradient step minimizing a given loss. We propose to add a second step to maximize training generalization. To…
cs.CL2019
Byte-Pair Encoding for Text-to-SQL Generation
Samuel Müller, Andreas Vlachos
Neural sequence-to-sequence models provide a competitive approach to the task of mapping a question in natural language to an SQL query, also referred to as text-to-SQL generation.…