Jun Sese
Molecular Biology · 2018–2020
A researcher in the Science corpus: 7 papers; trusts 20 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 149 citations into it; #10,747 of 17,773 by trust here, #11,019 by citations
- Artificial Intelligence — 68 citations into it
- Computer Vision and Pattern Recognition — 40 citations into it
- Computational Theory and Mathematics — 31 citations into it; #8,818 of 12,182 by trust here, #5,954 by citations
- Materials Chemistry — 8 citations into it
- Computational Chemistry & Drug Discovery — 6 citations into it
- Evaluation & Benchmarks — 5 citations into it
- Model Architectures — 5 citations into it
Whom Jun Sese trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoon Kim
- Mehmet Gönen
- Geoffrey E. Hinton
- Dzmitry Bahdanau
- Yoshua Bengio
- Fangping Wan
- Jimmy Ba
Who trusts Jun Sese overall
Papers from Jun Sese's perspective
Ordered by how strongly the researchers Jun Sese trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences (2018) — 630 citations
- Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data (2020) — 65 citations
- Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer (2020) — 48 citations
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020) — 149 citations
- Multiple wheat genomes reveal global variation in modern breeding (2020) — 715 citations
- Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy (2019) — 244 citations
- ChIP‐Atlas: a data‐mining suite powered by full integration of public ChIP‐seq data (2018) — 657 citations