Badri Adhikari
Molecular Biology · 2015–2019
A researcher in the Science corpus: 8 papers; trusts 106 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 181 citations into it; #1,011 of 17,773 by trust here, #4,951 by citations
- Computational Biology & Bioinformatics — 108 citations into it; #171 of 660 by trust here, #396 by citations
- Materials Chemistry — 88 citations into it; #1,889 of 11,513 by trust here, #7,227 by citations
- Computational Theory and Mathematics — 15 citations into it
- Spectroscopy — 15 citations into it
- Artificial Intelligence — 9 citations into it
- Model Architectures — 9 citations into it; #1,288 of 2,278 by trust here, #1,601 by citations
- Computer Vision and Pattern Recognition — 6 citations into it
Whom Badri Adhikari trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David T. Jones
- T. Kościółek
- David C. Jones
- Johannes Söding
- Jinbo Xu
- Mirco Michel
- Yang Zhang
- Jianlin Cheng
- Bohdan Monastyrskyy
- Sheng Wang
Who trusts Badri Adhikari overall
- Jianlin Cheng
- Roshan Rao
- Jinbo Xu
- Sheng Wang
- Mohammed Alquraishi
- Joe G. Greener
- Claudio Mirabello
- Mirco Michel
- Jie Hou
- Mirko Torrisi
Papers from Badri Adhikari's perspective
Ordered by how strongly the researchers Badri Adhikari trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Dropout: a simple way to prevent neural networks from overfitting
- Going deeper with convolutions
- Rectified Linear Units Improve Restricted Boltzmann Machines
Their papers
- DNCON2: improved protein contact prediction using two-level deep convolutional neural networks (2017) — 148 citations
- DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019) — 47 citations
- CONFOLD: Residue-Residue Contact-guided ab initio Protein Folding (2015) — 147 citations
- CONFOLD2: improved contact-driven ab initio protein structure modeling (2017) — 72 citations
- Large-scale model quality assessment for improving protein tertiary structure prediction (2015) — 69 citations
- ConEVA: a toolbox for comprehensive assessment of protein contacts (2016) — 23 citations
- DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017) — 228 citations
- Analysis of several key factors influencing deep learning-based inter-residue contact prediction (2019) — 35 citations