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Djoerd Hiemstra, Christin Seifert, Dolf Trieschnigg, Jan Trienes - 2020
Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove personally identifying information from these medical records. However, due to the unavailability of labeled dat...
Pascal Poupart, Agastya Kalra, Rishav Agarwal, Abdullah Rashwan - 2020
We present MatrixNets (xNets), a new deep architecture for object detection. xNets map objects with similar sizes and aspect ratios into many specialized layers, allowing xNets to provide a scale and aspect ratio aware architecture. We leverage xNets to enhance single-stage object detection frameworks. First, we apply xNets on anchor-based object...
Daniel Hernández-Lobato, Eduardo C. Garrido-Merchán, Bryan Zaldivar, Carlos Villacampa-Calvo - 2020
It is a common practice in the supervised machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are common in real problems, as measurements are never perfectly accurate. If this input noise is not taken into account, a supervised machine learning method is exp...
Dagmar Kainmueller, Peter Hirsch - 2020
Segmentation of cell nuclei in microscopy images is a prevalent necessity in cell biology. Especially for three-dimensional datasets, manual segmentation is prohibitively time-consuming, motivating the need for automated methods. Learning-based methods trained on pixel-wise ground-truth segmentations have been shown to yield state-of-the-art resu...
Haoping Bai, Jianing Yang, Xueying Ding, Yue Zhao - 2020
Outlier detection is a key field of machine learning for identifying abnormal data objects. Due to the high expense of acquiring ground truth, unsupervised models are often chosen in practice. To compensate for the unstable nature of unsupervised algorithms, practitioners from high-stakes fields like finance, health, and security, prefer to build...
Giuseppe Amato, Claudio Gennaro, Fabrizio Falchi, Nicola Messina, Luca Ciampi - 2020
Pedestrian detection through computer vision is a building block for a multitude of applications in the context of smart cities, such as surveillance of sensitive areas, personal safety, monitoring, and control of pedestrian flow, to mention only a few. Recently, there was an increasing interest in deep learning architectures for performing such ...
Tanja Zseby, Joachim Fabini, Fares Meghdouri, Maximilian Bachl - 2020
Recurrent Neural Networks (RNNs) have been shown to be valuable for constructing Intrusion Detection Systems (IDSs) for network data. They allow determining if a flow is malicious or not already before it is over, making it possible to take action immediately. However, considering the large number of packets that have to be inspected, the questio...
Sarika Jalan, M Tanveer, Naveen Mendola, Saptarshi Ghosh, M. A. Ganaie - 2020
Coupled dynamics on the network models have been tremendously helpful in getting insight into complex spatiotemporal dynamical patterns of a wide variety of large-scale real-world complex systems. Chimera, a state of coexistence of incoherence and coherence, is one of such patterns arising in identically coupled oscillators, which has recently dr...
Doina Caragea, Cornelia Caragea, Jishnu Ray Chowdhury - 2020
Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic iden...
Joost van de Weijer, Adriana Romero, Mikel Menta - 2020
Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain. The great majority of existing domain adaptation models rely on image tr...
