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Found 6 papers, 3 papers with code
Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram
However, adapting to the application of screening disease is challenging in that labeled ECG data are limited.
PePe: Personalized Post-editing Model utilizing User-generated Post-edits
Incorporating personal preference is crucial in advanced machine translation tasks.
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains.
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
The former normalizes the input to fix its distribution in terms of the mean and variance, while the latter returns the output to the original distribution.
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences.
