Michal Marianne Amitai
Computer Vision and Pattern Recognition · 2018–2019
A researcher in the Science corpus: 4 papers; trusts 7 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 59 citations into it; #13,084 of 21,575 by trust here, #5,333 by citations
- Artificial Intelligence — 49 citations into it; #21,051 of 35,345 by trust here, #7,172 by citations
Whom Michal Marianne Amitai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Michal Marianne Amitai overall
Papers from Michal Marianne Amitai's perspective
Ordered by how strongly the researchers Michal Marianne Amitai trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Dropout: a simple way to prevent neural networks from overfitting
- Gradient-based learning applied to document recognition
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Synthetic data augmentation using GAN for improved liver lesion classification (2018) — 847 citations
- The Liver Tumor Segmentation Benchmark (LiTS) (2019) — 1,500 citations
- GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification (2018) — 1,883 citations
- Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. (2019) — 469 citations