3,257 research outputs found
Delay-Coordinates Embeddings as a Data Mining Tool for Denoising Speech Signals
In this paper we utilize techniques from the theory of non-linear dynamical
systems to define a notion of embedding threshold estimators. More specifically
we use delay-coordinates embeddings of sets of coefficients of the measured
signal (in some chosen frame) as a data mining tool to separate structures that
are likely to be generated by signals belonging to some predetermined data set.
We describe a particular variation of the embedding threshold estimator
implemented in a windowed Fourier frame, and we apply it to speech signals
heavily corrupted with the addition of several types of white noise. Our
experimental work seems to suggest that, after training on the data sets of
interest,these estimators perform well for a variety of white noise processes
and noise intensity levels. The method is compared, for the case of Gaussian
white noise, to a block thresholding estimator
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Neural Nearest Neighbors Networks
Non-local methods exploiting the self-similarity of natural signals have been
well studied, for example in image analysis and restoration. Existing
approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed
feature space. The main hurdle in optimizing this feature space w.r.t.
application performance is the non-differentiability of the KNN selection rule.
To overcome this, we propose a continuous deterministic relaxation of KNN
selection that maintains differentiability w.r.t. pairwise distances, but
retains the original KNN as the limit of a temperature parameter approaching
zero. To exploit our relaxation, we propose the neural nearest neighbors block
(N3 block), a novel non-local processing layer that leverages the principle of
self-similarity and can be used as building block in modern neural network
architectures. We show its effectiveness for the set reasoning task of
correspondence classification as well as for image restoration, including image
denoising and single image super-resolution, where we outperform strong
convolutional neural network (CNN) baselines and recent non-local models that
rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at
https://github.com/visinf/n3net
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