Mostafa Karimi

Molecular Biology · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 120 citations into it
• Computational Biology & Bioinformatics — 47 citations into it
• Materials Chemistry — 29 citations into it
• Computational Theory and Mathematics — 25 citations into it
• Artificial Intelligence — 16 citations into it
• Computational Chemistry & Drug Discovery — 12 citations into it
• Natural Language Processing — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it

Whom Mostafa Karimi trusts overall

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

1. Ilya Sutskever
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. David Weininger
5. Yoshua Bengio
6. Yang Zhang
7. Oriol Vinyals
8. Bernhard Schölkopf
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Mostafa Karimi overall

1. Yue Cao
2. Hannes Stärk
3. Ali Madani
4. Raphael J. L. Townshend
5. Thomas A. Geddes
6. Octavian-Eugen Ganea

Papers from Mostafa Karimi's perspective

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

Their papers

• De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks (2020) — 80 citations
• DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks (2018) — 441 citations
• Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018) — 959 citations
• De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN) (2019) — 11 citations