Search Results for author:
Found 22 papers, 7 papers with code
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods.
Ranked #1 on
Data Augmentation
on CIFAR-10
Nystrom Method for Accurate and Scalable Implicit Differentiation
The essential difficulty of gradient-based bilevel optimization using implicit differentiation is to estimate the inverse Hessian vector product with respect to neural network parameters.
MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing.
noisy quantum circuit classification (quantum ML, error mitigation)
quantum circuit classification (classical ML)
+1
Will Large-scale Generative Models Corrupt Future Datasets?
These trends lead us to a research question: "\textbf{will such generated images impact the quality of future datasets and the performance of computer vision models positively or negatively?}"
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection
In addition, we devise a metric to evaluate the anatomical fidelity of the reconstructed images and confirm that the overall detection performance is improved when the image reconstruction network achieves a higher score.
Investigating CNNs' Learning Representation under label noise
Deep convolutional neural networks (CNNs) are known to be robust against label noise on extensive datasets.
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Prostate cancer is the most common cancer among US men.
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.
Meta Approach to Data Augmentation Optimization
Data augmentation policies drastically improve the performance of image recognition tasks, especially when the policies are optimized for the target data and tasks.
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness
Convolutional Neural Networks (CNNs) are vulnerable to unseen noise on input images at the test time, and thus improving the robustness is crucial.
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval
Medical images can be decomposed into normal and abnormal features, which is considered as the compositionality.
Graph Energy-based Model for Substructure Preserving Molecular Design
It is common practice for chemists to search chemical databases based on substructures of compounds for finding molecules with desired properties.
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval
To support comparative diagnostic reading, content-based image retrieval (CBIR), which can selectively utilize normal and abnormal features in medical images as two separable semantic components, will be useful.
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels
Optimizing hyperparameters of machine learning algorithms especially for limited labeled data is important but difficult, because then obtaining enough validation data is practically impossible.
Unifying semi-supervised and robust learning by mixup
In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt.
Graph Energy-based Model for Molecular Graph Generation
We present Graph Energy-based Model (GEM), an energy-based model for molecular graph generation.
Noncommutative $C^*$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^*$-algebra
We propose a new generalization of neural networks with noncommutative $C^*$-algebra.
Sketch-based Medical Image Retrieval
As a result, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples.
An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
In the realm of out-of-distribution (OOD) generalization tasks, fine-tuning pre-trained models has become a prevalent strategy.
Glocal Hypergradient Estimation with Koopman Operator
Through numerical experiments of hyperparameter optimization, including optimization of optimizers, we demonstrate the effectiveness of the glocal hypergradient estimation.
Self-attention Networks Localize When QK-eigenspectrum Concentrates
To this end, we characterize the notion of attention localization by the eigenspectrum of query-key parameter matrices and reveal that a small eigenspectrum variance leads attention to be localized.
Quantum Circuit $C^*$-algebra Net
This interaction enables the circuits to share information among them, which contributes to improved generalization performance in machine learning tasks.
