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Mutual-Learning Improves End-to-End Speech Translation
In this paper, we propose an alternative–a trainable mutual-learning scenario, where the MT and the ST models are collaboratively trained and are considered as peers, rather than teacher/student.
Non-Linearity in Mapping Based Cross-Lingual Word Embeddings
Recent works on cross-lingual word embeddings have been mainly focused on linear-mapping-based approaches, where pre-trained word embeddings are mapped into a shared vector space using a linear transformation.
Robust Deep Sensing Through Transfer Learning in Cognitive Radio
We propose a robust spectrum sensing framework based on deep learning.
Comparison of Neural Network Architectures for Spectrum Sensing
Given abundant training data and computational and memory resources, CNN, RNN, and BiRNN are shown to achieve similar performance.
A Neural Network Detector for Spectrum Sensing under Uncertainties
The result displays the neural network's potential in exploiting implicit and incomplete knowledge about the signal's structure.
Individual common dolphin identification via metric embedding learning
Photo-identification (photo-id) of dolphin individuals is a commonly used technique in ecological sciences to monitor state and health of individuals, as well as to study the social structure and distribution of a population.
