26,768 research outputs found
Factorized Topic Models
In this paper we present a modification to a latent topic model, which makes
the model exploit supervision to produce a factorized representation of the
observed data. The structured parameterization separately encodes variance that
is shared between classes from variance that is private to each class by the
introduction of a new prior over the topic space. The approach allows for a
more eff{}icient inference and provides an intuitive interpretation of the data
in terms of an informative signal together with structured noise. The
factorized representation is shown to enhance inference performance for image,
text, and video classification.Comment: ICLR 201
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
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