716 citations · 825 across the 31 of their papers we have counts for
9 papers · 1 filter
Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure
Jiseok Lee, Brian Kenji Iwana
Transfer learning is an effective technique for addressing data scarcity in deep learning for time series classification, but its success depends on the selection of source dataset…
Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing
Yataro Tamura, Brian Kenji Iwana, Jiseok Lee
Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications. However, their vulnerability to adversari…
Towards Time Series Generation Conditioned on Unstructured Natural Language
Jaeyun Woo, Jiseok Lee, Brian Kenji Iwana
Generative Artificial Intelligence (AI) has rapidly become a powerful tool, capable of generating various types of data, such as images and text. However, despite the significant a…
Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
Jiseok Lee, Brian Kenji Iwana
Transfer learning is a common practice that alleviates the need for extensive data to train neural networks. It is performed by pre-training a model using a source dataset and fine…
On Mini-Batch Training with Varying Length Time Series
Brian Kenji Iwana
In real-world time series recognition applications, it is possible to have data with varying length patterns. However, when using artificial neural networks (ANN), it is standard p…
Dynamic Data Augmentation with Gating Networks for Time Series Recognition
Daisuke Oba, Shinnosuke Matsuo, Brian Kenji Iwana
Data augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method i…
An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks
Brian Kenji Iwana, Seiichi Uchida
In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase…
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher
Brian Kenji Iwana, Seiichi Uchida
Neural networks have become a powerful tool in pattern recognition and part of their success is due to generalization from using large datasets. However, unlike other domains, time…
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
Kumar Shridhar, Joonho Lee, Hideaki Hayashi +6
Activation functions play an important role in training artificial neural networks. The majority of currently used activation functions are deterministic in nature, with their fixe…