Karolis Uziela
Molecular Biology · 2016–2019
A researcher in the Science corpus: 6 papers; trusts 35 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 112 citations into it; #4,028 of 17,773 by trust here, #8,480 by citations
- Materials Chemistry — 65 citations into it; #5,097 of 11,513 by trust here, #7,964 by citations
- Computational Biology & Bioinformatics — 48 citations into it
- Computational Theory and Mathematics — 22 citations into it; #8,387 of 12,182 by trust here, #9,804 by citations
- Spectroscopy — 10 citations into it; #663 of 1,674 by trust here, #844 by citations
- Evaluation & Benchmarks — 9 citations into it
Whom Karolis Uziela trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Björn Wallner
- David C. Jones
- Arne Elofsson
- Matthew D. Zeiler
- Andriy Kryshtafovych
- Jianlin Cheng
- Xavier Glorot
- Renzhi Cao
- Arjun Ray
- Dmitrij Frishman
Who trusts Karolis Uziela overall
- Mirco Michel
- Georgy Derevyanko
- David Ménendez Hurtado
- Arne Elofsson
- Bowen Jing
- Liam J. McGuffin
- Sergei Grudinin
- Jianlin Cheng
- Raphael J. L. Townshend
- Jonghun Won
Papers from Karolis Uziela's perspective
Ordered by how strongly the researchers Karolis Uziela 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
- Dropout: a simple way to prevent neural networks from overfitting
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Understanding the difficulty of training deep feedforward neural networks
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- ProQ3: Improved model quality assessments using Rosetta energy terms (2016) — 101 citations
- Methods for estimation of model accuracy in CASP12 (2017) — 32 citations
- Large-scale structure prediction by improved contact predictions and model quality assessment (2017) — 34 citations
- Estimation of model accuracy in CASP13 (2019) — 75 citations
- ProQ3D: improved model quality assessments using deep learning (2016) — 159 citations
- Deep transfer learning in the assessment of the quality of protein models (2018) — 36 citations