Osamu Abe

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Model Architectures — 6 citations into it

Whom Osamu Abe trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio

Papers from Osamu Abe's perspective

Ordered by how strongly the researchers Osamu Abe trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. Dropout: a simple way to prevent neural networks from overfitting
4. ImageNet classification with deep convolutional neural networks
5. Rectified Linear Units Improve Restricted Boltzmann Machines
6. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
7. Multitask Learning
8. Gradient-based learning applied to document recognition
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)

Their papers

• Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study. (2017) — 595 citations
• Deep learning with convolutional neural network in radiology (2018) — 346 citations
• Effect of Gadolinium on the Estimation of Myelin and Brain Tissue Volumes Based on Quantitative Synthetic MRI (2018)