Daniele Ramazzotti

Molecular Biology · 2015–2016

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 22 citations into it; #7,799 of 17,773 by trust here, #11,608 by citations
• Artificial Intelligence — 19 citations into it
• AI Ethics, Fairness & Society — 7 citations into it; #148 of 494 by trust here, #271 by citations
• Computer Vision and Pattern Recognition — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Daniele Ramazzotti trusts overall

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

1. Michael I. Jordan
2. Salvatore Ruggieri
3. Geoffrey E. Hinton
4. L. V. D. van der Maaten
5. Dino Pedreschi
6. Brendan J. Frey
7. Daniel D. Lee
8. Francis R. Bach
9. Chris H. Q. Ding
10. Emma Pierson

Who trusts Daniele Ramazzotti overall

1. Lu Zhang
2. Yongkai Wu
3. Romain Lopez
4. Xintao Wu
5. Silvia Chiappa
6. Indrė Žliobaitė
7. Malte D. Luecken

Papers from Daniele Ramazzotti's perspective

Ordered by how strongly the researchers Daniele Ramazzotti 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. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning (2016) — 650 citations
• Exposing the probabilistic causal structure of discrimination (2015) — 78 citations