Sai Pooja Mahajan
Molecular Biology · 2020–2022
A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 147 citations into it; #13,077 of 17,773 by trust here, #13,891 by citations
- Computational Biology & Bioinformatics — 70 citations into it
- Materials Chemistry — 66 citations into it
- Artificial Intelligence — 43 citations into it
- Computational Theory and Mathematics — 29 citations into it
- Computer Vision and Pattern Recognition — 19 citations into it
- Radiology, Nuclear Medicine and Imaging — 11 citations into it; #3,176 of 3,265 by trust here, #3,066 by citations
Whom Sai Pooja Mahajan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Michael I. Jordan
- Geoffrey E. Hinton
- Diederik P. Kingma
- Sheng Wang
- Yann LeCun
- Alex Krizhevsky
- Kaiming He
- Ilya Sutskever
- Radford M. Neal
Who trusts Sai Pooja Mahajan overall
Papers from Sai Pooja Mahajan's perspective
Ordered by how strongly the researchers Sai Pooja Mahajan trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies (2022) — 344 citations
- Deep Learning in Protein Structural Modeling and Design (2020) — 191 citations
- Improved antibody structure prediction by deep learning of side chain conformations (2021) — 6 citations