![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2021_0.jpg?itok=5O5H7jAd)
- Read more about MULTIVIEW SENSING WITH UNKNOWN PERMUTATIONS: AN OPTIMAL TRANSPORT APPROACH
- Log in to post comments
In several applications, including imaging of deformable objects while in motion, simultaneous localization and mapping, and unlabeled sensing, we encounter the problem of recovering a signal that is measured subject to unknown permutations. In this paper we take a fresh look at this problem through the lens of optimal transport (OT). In particular, we recognize that in most practical applications the unknown permutations are not arbitrary but some are more likely to occur than others.
- Categories:
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2021_0.jpg?itok=5O5H7jAd)
- Read more about Moving object classification with a sub-6 GHz massive MIMO array using real data
- Log in to post comments
- Categories:
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2021_0.jpg?itok=5O5H7jAd)
- Read more about MORE:A Metric-learning based Framework for Open-domain Relation Extraction
- Log in to post comments
Open relation extraction (OpenRE) is the task of extracting relation schemes from open-domain corpora. Most existing OpenRE methods either do not fully benefit from high-quality labeled corpora or can not learn semantic representation directly, affecting downstream clustering efficiency. To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction.
- Categories:
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2021_0.jpg?itok=5O5H7jAd)
During the COVID-19 pandemic the health authorities at airports and train stations try to screen and identify the travellers possibly exposed to the virus. However, many individuals avoid getting tested and hence may misreport their travel history. This is a challenge for the health authorities who wish to ascertain the truly susceptible cases in spite of this strategic misreporting. We investigate the problem of questioning travellers to classify them for further testing when the travellers are strategic or are unwilling to reveal their travel histories.
- Categories:
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2021_0.jpg?itok=5O5H7jAd)
During the COVID-19 pandemic the health authorities at airports and train stations try to screen and identify the travellers possibly exposed to the virus. However, many individuals avoid getting tested and hence may misreport their travel history. This is a challenge for the health authorities who wish to ascertain the truly susceptible cases in spite of this strategic misreporting. We investigate the problem of questioning travellers to classify them for further testing when the travellers are strategic or are unwilling to reveal their travel histories.
- Categories:
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2021_0.jpg?itok=5O5H7jAd)
- Categories:
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2021_0.jpg?itok=5O5H7jAd)
- Read more about A PARTIALLY COLLAPSED GIBBS SAMPLER FOR UNSUPERVISED NONNEGATIVE SPARSE SIGNAL RESTORATION
- Log in to post comments
In this paper the problem of restoration of unsupervised nonnegative sparse signals is addressed in the Bayesian framework. We introduce a new probabilistic hierarchical prior, based on the Generalized Hyperbolic (GH) distribution, which explicitly accounts for sparsity. On the one hand, this new prior allows us to take into account the non-negativity.
- Categories:
![](https://sigport.org/sites/default/files/styles/home/public/DCC_2021_0.jpg?itok=NdeMrA5c)
- Read more about Neural Network Compression via Additive Combination of Reshaped, Low-rank Matrices
- Log in to post comments
- Categories: