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Found 14 papers, 3 papers with code
Validity problems in clinical machine learning by indirect data labeling using consensus definitions
We demonstrate a validity problem of machine learning in the vital application area of disease diagnosis in medicine.
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation
Data augmentation is a technique to generate new training data based on existing data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis
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 decision.
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation
End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language.
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR
Our method, called Aligned Data Augmentation (ADA) for ASR, replaces transcribed tokens and the speech representations in an aligned manner to generate previously unseen training pairs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Embedding Meta-Textual Information for Improved Learning to Rank
Neural approaches to learning term embeddings have led to improved computation of similarity and ranking in information retrieval (IR).
Cascaded Models With Cyclic Feedback For Direct Speech Translation
Direct speech translation describes a scenario where only speech inputs and corresponding translations are available.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction
Sepsis is the leading cause of death in non-coronary intensive care units.
Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation
We propose an interactive-predictive neural machine translation framework for easier model personalization using reinforcement and imitation learning.
Cross-Lingual Learning-to-Rank with Shared Representations
Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query.
Multimodal Pivots for Image Caption Translation
We present an approach to improve statistical machine translation of image descriptions by multimodal pivots defined in visual space.
