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Improving Transformers using Faithful Positional Encoding
We propose a new positional encoding method for a neural network architecture called the Transformer.
Diagnostic Spatio-temporal Transformer with Faithful Encoding
This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency.
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss
To overcome this issue, this paper presents Distribution Aware Deep Guided Counterfactual Explanations (DA-DGCEx), which adds a term to the DGCEx cost function that penalizes out of distribution counterfactual instances.
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