Search Results for author:
Found 10 papers, 2 papers with code
Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis.
ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models
This study aims to solve the unsupervised outlier detection problem where training data contain outliers, but any label information about inliers and outliers is not given.
Learning fair representation with a parametric integral probability metric
That is, we derive theoretical relations between the fairness of representation and the fairness of the prediction model built on the top of the representation (i. e., using the representation as the input).
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
By carrying out various experiments, we demonstrate that the INN method resolves the shortcomings in the memorization effect successfully and thus is helpful to construct more accurate deep prediction models with training data with noisy labels.
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models
In the considered model, a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity.
Kernel-convoluted Deep Neural Networks with Data Augmentation
The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robustness to adversarial examples.
Understanding and Improving Virtual Adversarial Training
In semi-supervised learning, virtual adversarial training (VAT) approach is one of the most attractive method due to its intuitional simplicity and powerful performances.
Fast convergence rates of deep neural networks for classification
In addition, we consider a DNN classifier learned by minimizing the cross-entropy, and show that the DNN classifier achieves a fast convergence rate under the condition that the conditional class probabilities of most data are sufficiently close to either 1 or zero.
On variation of gradients of deep neural networks
We provide a theoretical explanation of the role of the number of nodes at each layer in deep neural networks.
Fast adversarial training for semi-supervised learning
The proposed method generates bad samples of high-quality by use of the adversarial training used in VAT.
