1,105 research outputs found
A Deep Learning Approach to Structured Signal Recovery
In this paper, we develop a new framework for sensing and recovering
structured signals. In contrast to compressive sensing (CS) systems that employ
linear measurements, sparse representations, and computationally complex
convex/greedy algorithms, we introduce a deep learning framework that supports
both linear and mildly nonlinear measurements, that learns a structured
representation from training data, and that efficiently computes a signal
estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an
unsupervised feature learner. SDA enables us to capture statistical
dependencies between the different elements of certain signals and improve
signal recovery performance as compared to the CS approach
Compressing Word Embeddings
Recent methods for learning vector space representations of words have
succeeded in capturing fine-grained semantic and syntactic regularities using
vector arithmetic. However, these vector space representations (created through
large-scale text analysis) are typically stored verbatim, since their internal
structure is opaque. Using word-analogy tests to monitor the level of detail
stored in compressed re-representations of the same vector space, the
trade-offs between the reduction in memory usage and expressiveness are
investigated. A simple scheme is outlined that can reduce the memory footprint
of a state-of-the-art embedding by a factor of 10, with only minimal impact on
performance. Then, using the same `bit budget', a binary (approximate)
factorisation of the same space is also explored, with the aim of creating an
equivalent representation with better interpretability.Comment: 10 pages, 0 figures, submitted to ICONIP-2016. Previous experimental
results were submitted to ICLR-2016, but the paper has been significantly
updated, since a new experimental set-up worked much bette
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
High-dimensional time series are common in many domains. Since human
cognition is not optimized to work well in high-dimensional spaces, these areas
could benefit from interpretable low-dimensional representations. However, most
representation learning algorithms for time series data are difficult to
interpret. This is due to non-intuitive mappings from data features to salient
properties of the representation and non-smoothness over time. To address this
problem, we propose a new representation learning framework building on ideas
from interpretable discrete dimensionality reduction and deep generative
modeling. This framework allows us to learn discrete representations of time
series, which give rise to smooth and interpretable embeddings with superior
clustering performance. We introduce a new way to overcome the
non-differentiability in discrete representation learning and present a
gradient-based version of the traditional self-organizing map algorithm that is
more performant than the original. Furthermore, to allow for a probabilistic
interpretation of our method, we integrate a Markov model in the representation
space. This model uncovers the temporal transition structure, improves
clustering performance even further and provides additional explanatory
insights as well as a natural representation of uncertainty. We evaluate our
model in terms of clustering performance and interpretability on static
(Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST
images, a chaotic Lorenz attractor system with two macro states, as well as on
a challenging real world medical time series application on the eICU data set.
Our learned representations compare favorably with competitor methods and
facilitate downstream tasks on the real world data.Comment: Accepted for publication at the Seventh International Conference on
Learning Representations (ICLR 2019
Discriminative Recurrent Sparse Auto-Encoders
We present the discriminative recurrent sparse auto-encoder model, comprising
a recurrent encoder of rectified linear units, unrolled for a fixed number of
iterations, and connected to two linear decoders that reconstruct the input and
predict its supervised classification. Training via
backpropagation-through-time initially minimizes an unsupervised sparse
reconstruction error; the loss function is then augmented with a discriminative
term on the supervised classification. The depth implicit in the
temporally-unrolled form allows the system to exhibit all the power of deep
networks, while substantially reducing the number of trainable parameters.
From an initially unstructured network the hidden units differentiate into
categorical-units, each of which represents an input prototype with a
well-defined class; and part-units representing deformations of these
prototypes. The learned organization of the recurrent encoder is hierarchical:
part-units are driven directly by the input, whereas the activity of
categorical-units builds up over time through interactions with the part-units.
Even using a small number of hidden units per layer, discriminative recurrent
sparse auto-encoders achieve excellent performance on MNIST.Comment: Added clarifications suggested by reviewers. 15 pages, 10 figure
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