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Rozenn Dahyot, Vladimir A. Krylov, Matej Ulicny - 2020
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In this paper we propose to revert to learning combinations of preset spectral filters by switching to CNNs with harmonic blocks. We rely on the use of the Discrete Cosine Transform (DCT) filters which have excellent energy compacti...
Matthias Zwicker, L. Andrea Dunbar, Tiziano Portenier, Geng Lin, Siavash A. Bigdeli - 2020
Learning generative probabilistic models that can estimate the continuous density given a set of samples, and that can sample from that density, is one of the fundamental challenges in unsupervised machine learning. In this paper we introduce a new approach to obtain such models based on what we call denoising density estimators (DDEs). A DDE is ...
Kay Chen Tan, Shuo Wang, Tao Chen, Zilin Xiang, Ke Li - 2020
Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows data/knowledge from other projects to facilitate the model building at the current project, name...
Lei Du, Yingyu Lin, Chengkun Lang, Shixian Ning, Zhuang Liu1, Huiwei Zhou - 2020
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KB). KBs contain huge amounts of structured informa...
Jimeng Sun, Duen Horng Chau, Cao Xiao, Scott Freitas, Rahul Duggal - 2020
In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficien...
Eunsuk Kang, Christian Kästner - 2020
Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require ca...
Lei Zhang, Biao Wang, Xihan Wei, Zhonggui Chen, Canyu Le - 2020
The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few labeled data could not accurately represent the true data distribution. In this work, we use a sophisticated network...
Cari M. Whyne, David M. Burns - 2020
A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data between individual users, resulting in very poor performance of impersonal algorithms for some subjects. We present an approach to personalized activity recognition based on deep embeddings derived from a fully convolutio...
Renato Renner, Hans J. Briegel, Henrik Wilming, Lea M. Trenkwalder, Sofiene Jerbi, Raban Iten, Tony Metger, Hendrik Poulsen Nautrup - 2020
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most current machine learning techniques reflect statistical structure present in the training data; however, these methods do not allow us to specify explicit and operation...
Thomas Demeester, Chris Develder, Johannes Deleu, Amir Hadifar - 2020
Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a way to reduce the memory and computation footprint of these models has seen significant research attention in recent years. In this paper, we present a new method for \...
