10 citations · 48 across the 40 of their papers we have counts for
7 papers · 1 filter
Enhancing Reinforcement Learning with discrete interfaces to learn the Dyck Language
Florian Dietz, Dietrich Klakow
Even though most interfaces in the real world are discrete, no efficient way exists to train neural networks to make use of them, yet. We enhance an Interaction Network (a Reinforc…
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
Michael A. Hedderich, Benjamin Roth, Katharina Kann +3
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing exp…
Analysing the Noise Model Error for Realistic Noisy Label Data
Michael A. Hedderich, Dawei Zhu, Dietrich Klakow
Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors. A…
Learning Functions to Study the Benefit of Multitask Learning
Gabriele Bettgenhäuser, Michael A. Hedderich, Dietrich Klakow
We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks. MTL models are trained to optimize a set of related tasks jointly.…
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow
Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empiri…
Logit Pairing Methods Can Fool Gradient-Based Attacks
Marius Mosbach, Maksym Andriushchenko, Thomas Trost +2
Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods the…