Search Results for author:
Found 15 papers, 3 papers with code
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
This is because coordinate descent iteratively updates all the parameters in the objective until convergence.
GuP: Fast Subgraph Matching by Guard-based Pruning
Subgraph matching, which finds subgraphs isomorphic to a query, is the key to information retrieval from data represented as a graph.
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Regularized discrete optimal transport (OT) is a powerful tool to measure the distance between two discrete distributions that have been constructed from data samples on two different domains.
Permuton-induced Chinese Restaurant Process
Our main contribution is to introduce the notion of permutons into the well-known Chinese restaurant process (CRP) for sequence partitioning: a permuton is a probability measure on $[0, 1]\times [0, 1]$ and can be regarded as a geometric interpretation of the scaling limit of permutations.
Few-shot Learning for Unsupervised Feature Selection
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.
Meta-Learning for Relative Density-Ratio Estimation
The closed-form solution enables fast and effective adaptation to a few instances, and its differentiability enables us to train our model such that the expected test error for relative DRE can be explicitly minimized after adapting to a few instances.
Fast Subgraph Matching by Exploiting Search Failures
That is, our algorithm generates failure patterns when a partial embedding is found unable to become an isomorphic embedding.
Databases
Semi-supervised Anomaly Detection on Attributed Graphs
To learn node embeddings specialized for anomaly detection, in which there is a class imbalance due to the rarity of anomalies, the parameters of a GCN are trained to minimize the volume of a hypersphere that encloses the node embeddings of normal instances while embedding anomalous ones outside the hypersphere.
Fast Sparse Group Lasso
Block Coordinate Descent is a standard approach to obtain the parameters of Sparse Group Lasso, and iteratively updates the parameters for each parameter group.
Transfer Anomaly Detection by Inferring Latent Domain Representations
The proposed method can infer the anomaly detectors for target domains without re-training by introducing the concept of latent domain vectors, which are latent representations of the domains and are used for inferring the anomaly detectors.
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
Furthermore, we reveal that robust CNNs with Absum are more robust against transferred attacks due to decreasing the common sensitivity and against high-frequency noise than standard regularization methods.
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining
Our key idea is to introduce a priority term that identifies the importance of a layer; we can select unimportant layers according to the priority and erase them after the training.
Sigsoftmax: Reanalysis of the Softmax Bottleneck
On the basis of this analysis, we propose sigsoftmax, which is composed of a multiplication of an exponential function and sigmoid function.
Preventing Gradient Explosions in Gated Recurrent Units
This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters.
Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks
Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks.
