20 citations · 51 across the 11 of their papers we have counts for
11 papers
Few-shot Learning for Unsupervised Feature Selection
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually requir…
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…
Fast Subgraph Matching by Exploiting Search Failures
Junya Arai, Makoto Onizuka, Yasuhiro Fujiwara +1
Subgraph matching is a compute-intensive problem that asks to enumerate all the isomorphic embeddings of a query graph within a data graph. This problem is generally solved with ba…
Sequenced Route Query with Semantic Hierarchy
Yuya Sasaki, Yoshiharu Ishikawa, Yasuhiro Fujiwara +1
The trip planning query searches for preferred routes starting from a given point through multiple Point-of-Interests (PoI) that match user requirements. Although previous studies…
Efficient Network Reliability Computation in Uncertain Graphs
Yuya Sasaki, Yasuhiro Fujiwara, Makoto Onizuka
Network reliability is an important metric to evaluate the connectivity among given vertices in uncertain graphs. Since the network reliability problem is known as #P-complete, exi…
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…
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
Sekitoshi Kanai, Yasutoshi Ida, Yasuhiro Fujiwara +2
We propose Absum, which is a regularization method for improving adversarial robustness of convolutional neural networks (CNNs). Although CNNs can accurately recognize images, rece…
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining
Yasutoshi Ida, Yasuhiro Fujiwara
Residual Networks with convolutional layers are widely used in the field of machine learning. Since they effectively extract features from input data by stacking multiple layers, t…
Sigsoftmax: Reanalysis of the Softmax Bottleneck
Sekitoshi Kanai, Yasuhiro Fujiwara, Yuki Yamanaka +1
Softmax is an output activation function for modeling categorical probability distributions in many applications of deep learning. However, a recent study revealed that softmax can…
Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks
Yasutoshi Ida, Yasuhiro Fujiwara, Sotetsu Iwamura
Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks. RMSProp offers efficient training since it uses first order gradients to approx…
Fast and Exact Top-k Search for Random Walk with Restart
Yasuhiro Fujiwara, Makoto Nakatsuji, Makoto Onizuka +1
Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity scor…