Nazim Bouatta

Molecular Biology · 2004–2023

A researcher in the Science corpus: 4 papers; trusts 5 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Molecular Biology — 102 citations into it
• Computational Biology & Bioinformatics — 57 citations into it
• Materials Chemistry — 45 citations into it
• Nuclear and High Energy Physics — 6 citations into it; #5,012 of 6,161 by trust here, #4,905 by citations

Whom Nazim Bouatta trusts overall

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

1. A. Sagnotti
2. Mohammed Alquraishi
3. X. Bekaert
4. Joshua Meier
5. Naomi Fox
6. James P. Roney
7. Nicolas Boulanger
8. M. Tsulaia
9. Ashish Vaswani
10. Diederik P. Kingma

Who trusts Nazim Bouatta overall

1. Dmitri Sorokin
2. Jeffrey A. Ruffolo
3. Kurt Hinterbichler
4. A. Sagnotti
5. Paolo Benincasa
6. D. Marolf
7. Patrick Bryant

Papers from Nazim Bouatta's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. ImageNet classification with deep convolutional neural networks
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Single-sequence protein structure prediction using language models from deep learning (2021) — 64 citations
• Single-sequence protein structure prediction using language models and deep learning (2022) — 358 citations
• OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization (2023) — 435 citations
• An Introduction to free higher-spin fields (2004) — 184 citations