These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e. g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e. g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions.
Ranked #2 on Question Answering on CronQuestions
Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA).
In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data.
Federated Learning Cryptography and Security
Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model.
However, there has been no attempt to exploit GNN to create taxonomies.
By synthesizing comprehensive information about support constraints and validation tests, improved risk evaluation can be achieved for randomized solutions in comparison with existing a posteriori bounds.
The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.
Ranked #27 on Link Prediction on FB15k-237
For evapotranspiration forecast error, the support vector clustering-based uncertainty set is adopted, which can be conveniently built from historical data.
The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution.
Especially, when certain samples miss an entire view of data, it creates the missing view problem.