4,029 research outputs found
Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on a
set of features computed by a deep, invertible transformation (i.e. a
normalizing flow). An attractive property of our model is that both
p(features), the density of the features, and p(targets | features), the
predictive distribution, can be computed exactly in a single feed-forward pass.
We show that our hybrid model, despite the invertibility constraints, achieves
similar accuracy to purely predictive models. Moreover the generative component
remains a good model of the input features despite the hybrid optimization
objective. This offers additional capabilities such as detection of
out-of-distribution inputs and enabling semi-supervised learning. The
availability of the exact joint density p(targets, features) also allows us to
compute many quantities readily, making our hybrid model a useful building
block for downstream applications of probabilistic deep learning.Comment: ICML 201
Combining Static and Dynamic Features for Multivariate Sequence Classification
Model precision in a classification task is highly dependent on the feature
space that is used to train the model. Moreover, whether the features are
sequential or static will dictate which classification method can be applied as
most of the machine learning algorithms are designed to deal with either one or
another type of data. In real-life scenarios, however, it is often the case
that both static and dynamic features are present, or can be extracted from the
data. In this work, we demonstrate how generative models such as Hidden Markov
Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can
be used to extract temporal information from the dynamic data. We explore how
the extracted information can be combined with the static features in order to
improve the classification performance. We evaluate the existing techniques and
suggest a hybrid approach, which outperforms other methods on several public
datasets.Comment: Presented at IEEE DSAA 201
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality
- …