13,832 research outputs found
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Multi-task learning leverages potential correlations among related tasks to
extract common features and yield performance gains. However, most previous
works only consider simple or weak interactions, thereby failing to model
complex correlations among three or more tasks. In this paper, we propose a
multi-task learning architecture with four types of recurrent neural layers to
fuse information across multiple related tasks. The architecture is
structurally flexible and considers various interactions among tasks, which can
be regarded as a generalized case of many previous works. Extensive experiments
on five benchmark datasets for text classification show that our model can
significantly improve performances of related tasks with additional information
from others
Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network
This paper proposes to use low-level spatial features extracted from
multichannel audio for sound event detection. We extend the convolutional
recurrent neural network to handle more than one type of these multichannel
features by learning from each of them separately in the initial stages. We
show that instead of concatenating the features of each channel into a single
feature vector the network learns sound events in multichannel audio better
when they are presented as separate layers of a volume. Using the proposed
spatial features over monaural features on the same network gives an absolute
F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and
2.7% on the TUT-SED 2009 dataset that is fifteen times larger.Comment: Accepted for IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2017
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