Search Results for author:
Found 26 papers, 23 papers with code
Integrating Weather Station Data and Radar for Precipitation Nowcasting: SmaAt-fUsion and SmaAt-Krige-GNet
This highlights the potential of incorporating discrete weather station data to enhance the performance of deep learning-based weather nowcasting models.
Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting
In this work, we leverage the benefits of self-supervised learning and integrate it with spatial-temporal learning to develop a novel model, SpaT-SparK.
Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding
This paper presents the novel Dual Stream Graph-Transformer Fusion (DS-GTF) architecture designed specifically for classifying task-based Magnetoencephalography (MEG) data.
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets.
GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcasting
This innovative framework paves the way for generative precipitation nowcasting using multiple data sources.
Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting
This capability enables handling complex, high-dimensional data and exploiting higher-order correlations between data dimensions.
A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification
Self-supervised learning addresses the challenge encountered by many supervised methods, i. e. the requirement of large amounts of annotated data.
TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start
Thanks to the domain adaptation capability of the proposed model, the domain shift between the source and target domain is minimized.
SAR-UNet: Small Attention Residual UNet for Explainable Nowcasting Tasks
The accuracy and explainability of data-driven nowcasting models are of great importance in many socio-economic sectors reliant on weather-dependent decision making.
WF-UNet: Weather Fusion UNet for Precipitation Nowcasting
In particular, we propose the Weather Fusion UNet (WF-UNet) model, which utilizes the Core 3D-UNet model and integrates precipitation and wind speed variables as input in the learning process and analyze its influences on the precipitation target task.
MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets
Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation.
BAST: Binaural Audio Spectrogram Transformer for Binaural Sound Localization
Accurate sound localization in a reverberation environment is essential for human auditory perception.
GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations
In this way, the proposed GCN-FFNN model learns from two types of input representations, i. e. grid and graph data, obtained via the discretization of the PDE domain.
AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks
Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting.
Multistream Graph Attention Networks for Wind Speed Forecasting
Reliable and accurate wind speed prediction has significant impact in many industrial sectors such as economic, business and management among others.
TENT: Tensorized Encoder Transformer for Temperature Forecasting
We show that compared to the classical encoder transformer, 3D convolutional neural networks, LSTM, and Convolutional LSTM, the proposed TENT model can better learn the underlying complex pattern of the weather data for the studied temperature prediction task.
Symbolic regression for scientific discovery: an application to wind speed forecasting
Symbolic regression corresponds to an ensemble of techniques that allow to uncover an analytical equation from data.
Broad-UNet: Multi-scale feature learning for nowcasting tasks
We introduce Broad-UNet, a novel architecture based on the core UNet model, to efficiently address this problem.
Deep Graph Convolutional Networks for Wind Speed Prediction
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.
Deep coastal sea elements forecasting using U-Net based models
The supply and demand of energy is influenced by meteorological conditions.
Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks
Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models.
Deep Neural-Kernel Machines
The convolutional pooling layer reduces the dimensionality of the multi-scale output representations.
SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture
Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere.
Wind speed prediction using multidimensional convolutional neural networks
In particular, we show that compared to classical CNN-based models, the proposed model is able to better characterise the spatio-temporal evolution of the wind data by learning the underlying complex input-output relationships from multiple dimensions (views) of the input data.
Deep brain state classification of MEG data
The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects.
Higher order Matching Pursuit for Low Rank Tensor Learning
In this paper, we propose higher order matching pursuit for low rank tensor learning problems with a convex or a nonconvex cost function, which is a generalization of the matching pursuit type methods.
