16 citations · 52 across the 22 of their papers we have counts for
10 papers · 1 filter
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +1
Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabeled data, has attrac…
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation
Tomoharu Iwata, Atsutoshi Kumagai
Although Gaussian processes (GPs) with deep kernels have been successfully used for meta-learning in regression tasks, its uncertainty estimation performance can be poor. We propos…
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space
Tomoharu Iwata, Atsutoshi Kumagai
Many neural network-based out-of-distribution (OoD) detection methods have been proposed. However, they require many training data for each target task. We propose a simple yet eff…
Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation
Kentaro Ohno, Atsutoshi Kumagai
Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to contr…
Meta-Learning for Relative Density-Ratio Estimation
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of th…
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection
Tomoharu Iwata, Atsutoshi Kumagai
Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural netw…
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Masanori Yamada, Sekitoshi Kanai, Tomoharu Iwata +4
Adversarial training is actively studied for learning robust models against adversarial examples. A recent study finds that adversarially trained models degenerate generalization p…
Few-shot Learning for Time-series Forecasting
Tomoharu Iwata, Atsutoshi Kumagai
Time-series forecasting is important for many applications. Forecasting models are usually trained using time-series data in a specific target task. However, sufficient data in the…
Semi-supervised Anomaly Detection on Attributed Graphs
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anoma…
Zero-shot Domain Adaptation without Domain Semantic Descriptors
Atsutoshi Kumagai, Tomoharu Iwata
We propose a method to infer domain-specific models such as classifiers for unseen domains, from which no data are given in the training phase, without domain semantic descriptors.…