Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstream tasks.
We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence.
Next, a dual discriminator based adversarial training procedure, which jointly considers an image discriminator that can maintain the local consistency at frame-level and a video discriminator that can enforce the global coherence of temporal dynamics, is employed to enhance the future frame prediction.
Measuring document similarity plays an important role in natural language processing tasks.
Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks.
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications.
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics.
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.
To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences.
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.
In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.
Information systems have widely been the target of malware attacks.
How to address the vulnerabilities and defense GNN against the adversarial attacks?
The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process.
Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.