97 citations · 180 across the 10 of their papers we have counts for
10 papers
Validity problems in clinical machine learning by indirect data labeling using consensus definitions
Michael Hagmann, Shigehiko Schamoni, Stefan Riezler
We demonstrate a validity problem of machine learning in the vital application area of disease diagnosis in medicine. It arises when target labels in training data are determined b…
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
Data augmentation is a technique to generate new training data based on existing data. We evaluate the simple and cost-effective method of concatenating the original data examples…
Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis
Shigehiko Schamoni, Michael Hagmann, Stefan Riezler
Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee d…
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language. Such data are notoriously scarce, makin…
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR
Tsz Kin Lam, Mayumi Ohta, Shigehiko Schamoni +1
We propose an on-the-fly data augmentation method for automatic speech recognition (ASR) that uses alignment information to generate effective training samples. Our method, called…
Embedding Meta-Textual Information for Improved Learning to Rank
Toshitaka Kuwa, Shigehiko Schamoni, Stefan Riezler
Neural approaches to learning term embeddings have led to improved computation of similarity and ranking in information retrieval (IR). So far neural representation learning has no…
Cascaded Models With Cyclic Feedback For Direct Speech Translation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
Direct speech translation describes a scenario where only speech inputs and corresponding translations are available. Such data are notoriously limited. We present a technique that…
Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction
Shigehiko Schamoni, Holger A. Lindner, Verena Schneider-Lindner +2
Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated…
Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
We propose an interactive-predictive neural machine translation framework for easier model personalization using reinforcement and imitation learning. During the interactive transl…
Multimodal Pivots for Image Caption Translation
Julian Hitschler, Shigehiko Schamoni, Stefan Riezler
We present an approach to improve statistical machine translation of image descriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval ove…