39 research outputs found
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
This work considers an estimation task in compressive sensing, where the goal
is to estimate an unknown signal from compressive measurements that are
corrupted by additive pre-measurement noise (interference, or clutter) as well
as post-measurement noise, in the specific setting where some (perhaps limited)
prior knowledge on the signal, interference, and noise is available. The
specific aim here is to devise a strategy for incorporating this prior
information into the design of an appropriate compressive measurement strategy.
Here, the prior information is interpreted as statistics of a prior
distribution on the relevant quantities, and an approach based on Bayesian
Experimental Design is proposed. Experimental results on synthetic data
demonstrate that the proposed approach outperforms traditional random
compressive measurement designs, which are agnostic to the prior information,
as well as several other knowledge-enhanced sensing matrix designs based on
more heuristic notions.Comment: 5 pages, 4 figures. Accepted for publication at The Asilomar
Conference on Signals, Systems, and Computers 201
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of
predefined ones is coined as document tagging in this work. Accurate tagging of
articles can benefit several downstream applications such as recommendation and
search. In this work, we propose a novel yet simple approach called DocTag2Vec
to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two
popular models for learning distributed representation of words and documents.
In DocTag2Vec, we simultaneously learn the representation of words, documents,
and tags in a joint vector space during training, and employ the simple
-nearest neighbor search to predict tags for unseen documents. In contrast
to previous multi-label learning methods, DocTag2Vec directly deals with raw
text instead of provided feature vector, and in addition, enjoys advantages
like the learning of tag representation, and the ability of handling newly
created tags. To demonstrate the effectiveness of our approach, we conduct
experiments on several datasets and show promising results against
state-of-the-art methods.Comment: 10 page
Towards Automated Single Channel Source Separation using Neural Networks
Many applications of single channel source separation (SCSS) including
automatic speech recognition (ASR), hearing aids etc. require an estimation of
only one source from a mixture of many sources. Treating this special case as a
regular SCSS problem where in all constituent sources are given equal priority
in terms of reconstruction may result in a suboptimal separation performance.
In this paper, we tackle the one source separation problem by suitably
modifying the orthodox SCSS framework and focus only on one source at a time.
The proposed approach is a generic framework that can be applied to any
existing SCSS algorithm, improves performance, and scales well when there are
more than two sources in the mixture unlike most existing SCSS methods.
Additionally, existing SCSS algorithms rely on fine hyper-parameter tuning
hence making them difficult to use in practice. Our framework takes a step
towards automatic tuning of the hyper-parameters thereby making our method
better suited for the mixture to be separated and thus practically more useful.
We test our framework on a neural network based algorithm and the results show
an improved performance in terms of SDR and SAR