检索结果-内蒙古大学图书馆

Broad-UNet: Multi-scale feature learning for nowcasting tasks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Fernández, Jesús García Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcasting has recently gained plenty of attention. In this paper, we treat the nowcasting problem as an image-to-image translation problem using satellite imagery. We introduce Broad-UNet, a novel architecture based on the core UNet model, to efficiently address this problem. In particular, the proposed Broad-UNet is equipped with asymmetric parallel convolutions as well as Atrous Spatial Pyramid Pooling (ASPP) module. In this way, The the Broad-UNet model learns more complex patterns by combining multi-scale features while using fewer parameters than the core UNet model. The proposed model is applied on two different nowcasting tasks, i.e. precipitation maps and cloud cover nowcasting. The obtained numerical results show that the introduced Broad-UNet model performs more accurate predictions compared to the other examined architectures. © 2021, CC BY-SA.

关键词： Weather forecasting

Deep multi-stations weather forecasting: Explainable recurrent convolutional neural networks

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Abdellaoui, Ismail Alaoui Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models. The present paper compares two different deep learning architectures to perform weather prediction on daily data gathered from 18 cities across Europe and spanned over a period of 15 years. We propose the Deep Attention Unistream Multistream (DAUM) networks that investigate different types of input representations (i.e. tensorial unistream vs. multistream) as well as the incorporation of the attention mechanism. In particular, we show that adding a self-attention block within the models increases the overall forecasting performance. Furthermore, visualization techniques such as occlusion analysis and score maximization are used to give an additional insight on the most important features and cities for predicting a particular target feature of target cities. © 2020, CC BY-SA.

关键词： Weather forecasting

Cost-Effective Training in Low-Resource Neural Machine Translation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Koneru, Sai Liu, Danni Niehues, Jan Department of Data Science and Knowledge Engineering Maastricht University Netherlands

While Active Learning (AL) techniques are explored in Neural Machine Translation (NMT), only a few works focus on tackling low annotation budgets where a limited number of sentences can get translated. Such situations are especially challenging and can occur for endangered languages with few human annotators or having cost constraints to label large amounts of data. Although AL is shown to be helpful with large budgets, it is not enough to build high-quality translation systems in these low-resource conditions. In this work, we propose a cost-effective training procedure to increase the performance of NMT models utilizing a small number of annotated sentences and dictionary entries. Our method leverages monolingual data with self-supervised objectives and a small-scale, inexpensive dictionary for additional supervision to initialize the NMT model before applying AL. We show that improving the model using a combination of these knowledge sources is essential to exploit AL strategies and increase gains in low-resource conditions. We also present a novel AL strategy inspired by domain adaptation for NMT and show that it is effective for low budgets. We propose a new hybrid data-driven approach, which samples sentences that are diverse from the labelled data and also most similar to unlabelled data. Finally, we show that initializing the NMT model and further using our AL strategy can achieve gains of up to 13 BLEU compared to conventional AL methods. © 2022, CC BY.

关键词： Neural machine translation

Deep Neural-Kernel Machines

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

In this chapter we review the main literature related to the recent advancement of deep neural-kernel architecture, an approach that seek the synergy between two powerful class of models, i.e. kernel-based models and artificial neural networks. The introduced deep neural-kernel framework is composed of a hybridization of the neural networks architecture and a kernel machine. More precisely, for the kernel counterpart the model is based on Least Squares Support Vector Machines with explicit feature mapping. Here we discuss the use of one form of an explicit feature map obtained by random Fourier features. Thanks to this explicit feature map, in one hand bridging the two architectures has become more straightforward and on the other hand one can find the solution of the associated optimization problem in the primal, therefore making the model scalable to large scale datasets. We begin by introducing a neural-kernel architecture that serves as the core module for deeper models equipped with different pooling layers. In particular, we review three neural-kernel machines with average, maxout and convolutional pooling layers. In average pooling layer the outputs of the previous representation layers are averaged. The maxout layer triggers competition among different input representations and allows the formation of multiple sub-networks within the same model. The convolutional pooling layer reduces the dimensionality of the multi-scale output representations. Comparison with neural-kernel model, kernel based models and the classical neural networks architecture have been made and the numerical experiments illustrate the effectiveness of the introduced models on several benchmark datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

Deep graph convolutional networks for wind speed prediction

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Stańczyk, Tomasz Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Wind speed prediction and forecasting is important for various business and management sectors. In this paper, we introduce new models for wind speed prediction based on graph convolutional networks (GCNs). Given hourly data of several weather variables acquired from multiple weather stations, wind speed values are predicted for multiple time steps ahead. In particular, the weather stations are treated as nodes of a graph whose associated adjacency matrix is learnable. In this way, the network learns the graph spatial structure and determines the strength of relations between the weather stations based on the historical weather data. We add a self-loop connection to the learnt adjacency matrix and normalize the adjacency matrix. We examine two scenarios with the self-loop connection setting (two separate models). In the first scenario, the self-loop connection is imposed as a constant additive. In the second scenario a learnable parameter is included to enable the network to decide about the self-loop connection strength. Furthermore, we incorporate data from multiple time steps with temporal convolution, which together with spatial graph convolution constitutes spatio-temporal graph convolution. We perform experiments on real datasets collected from weather stations located in cities in Denmark and the Netherlands. The numerical experiments show that our proposed models outperform previously developed baseline models on the referenced datasets. We provide additional insights by visualizing learnt adjacency matrices from each layer of our models. © 2021, CC BY-SA.

关键词： Convolution

Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Danni Spanakis, Gerasimos Niehues, Jan Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Encoder-decoder models provide a generic architecture for sequence-to-sequence tasks such as speech recognition and translation. While offline systems are often evaluated on quality metrics like word error rates (WER) and BLEU, latency is also a crucial factor in many practical use-cases. We propose three latency reduction techniques for chunk-based incremental inference and evaluate their efficiency in terms of accuracy-latency trade-off. On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by sacrificing 1% WER (6% rel.) compared to offline transcription. Although our experiments use the Transformer, the hypothesis selection strategies are applicable to other encoder-decoder models. To avoid expensive re-computation, we use a unidirectionally-attending encoder. After an adaptation procedure to partial sequences, the unidirectional model performs on-par with the original model. We further show that our approach is also applicable to low-latency speech translation. On How2 English-Portuguese speech translation, we reduce latency to 0.7 second (−84% rel.) while incurring a loss of 2.4 BLEU points (5% rel.) compared to the offline system. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

Unsupervised machine translation on dravidian languages

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Koneru, Sai Liu, Danni Niehues, Jan Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Unsupervised neural machine translation (UNMT) is beneficial especially for low resource languages such as those from the Dravidian family. However, UNMT systems tend to fail in realistic scenarios involving actual low resource languages. Recent works propose to utilize auxiliary parallel data and have achieved state-of-the-art results. In this work, we focus on unsupervised translation between English and Kannada, a low resource Dravidian language. We additionally utilize a limited amount of auxiliary data between English and other related Dravidian languages. We show that unifying the writing systems is essential in unsupervised translation between the Dravidian languages. We explore several model architectures that use the auxiliary data in order to maximize knowledge sharing and enable UNMT for distant language pairs. Our experiments demonstrate that it is crucial to include auxiliary languages that are similar to our focal language, Kannada. Furthermore, we propose a metric to measure language similarity and show that it serves as a good indicator for selecting the auxiliary languages. © 2021, CC BY.

关键词： Neural machine translation

Deep coastal sea elements forecasting using U-Net based models

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Fernández, Jesús García Abdellaoui, Ismail Alaoui Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

The supply and demand of energy is influenced by meteorological conditions. The relevance of accurate weather forecasts increases as the demand for renewable energy sources increases. The energy providers and policy makers require weather information to make informed choices and establish optimal plans according to the operational objectives. Due to the recent development of deep learning techniques applied to satellite imagery, weather forecasting that uses remote sensing data has also been the subject of major progress. The present paper investigates multiple steps ahead frame prediction for coastal sea elements in the Netherlands using U-Net based architectures. Hourly data from the Copernicus observation programme spanned over a period of 2 years has been used to train the models and make the forecasting, including seasonal predictions. We propose a variation of the U-Net architecture and further extend this novel model using residual connections, parallel convolutions and asymmetric convolutions in order to introduce three additional architectures. In particular, we show that the architecture equipped with parallel and asymmetric convolutions as well as skip connections outperforms the other three discussed models. © 2020, CC BY.

关键词： Convolution

Acute MI Detection Derived From ECG Parameters Distribution

学校读者我要写书评

暂无评论

Acute MI Detection Derived From ECG Parameters Distribution

Computers in Cardiology (CinC)

作者： Alfonso Aranda Joël Karel Pietro Bonizzi Ralf Peeters Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (数字)9781728169361

ISBN: (纸本)9781728159423

Several studies in the past have evaluated the use of different ECG-based features to diagnose acute myocardial infarction (AMI). This was generally done by looking at how well a feature reflects differences between baseline (no AMI) and AMI situations. This approach tends to overlook the progress of AMI and to underestimate false positives when implemented into a continuous monitoring setting and therefore appears inadequate for it. This has hindered the adoption of those methods in the clinical practice. In this research, we present a novel set of parameters for the dynamic assessment of AMI condition. Those parameters are obtained by analyzing the changes over time in the distribution properties of ECG-based features.

关键词： Standards Electrocardiography Myocardium databases Lead Market research Monitoring