157,545 research outputs found
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
LATTE: Application Oriented Social Network Embedding
In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the "appLicAtion
orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node "diffusive proximity" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl
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