5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Improving Transformers using Faithful Positional Encoding
Tsuyoshi Idé, Jokin Labaien, Pin-Yu Chen
We propose a new positional encoding method for a neural network architecture called the Transformer. Unlike the standard sinusoidal positional encoding, our approach is based on s…
cs.LG2023★ 5 cited
Diagnostic Spatio-temporal Transformer with Faithful Encoding
Jokin Labaien, Tsuyoshi Idé, Pin-Yu Chen +2
This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extr…
cs.LG2021
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss
Jokin Labaien, Ekhi Zugasti, Xabier De Carlos
Deep Learning has become a very valuable tool in different fields, and no one doubts the learning capacity of these models. Nevertheless, since Deep Learning models are often seen…