Vladimir Gligorijevic
Molecular Biology · 2017–2021
A researcher in the Science corpus: 4 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 87 citations into it
- Artificial Intelligence — 30 citations into it
- Computer Vision and Pattern Recognition — 20 citations into it
- Graph Learning — 16 citations into it
- Computational Theory and Mathematics — 10 citations into it
- Statistical and Nonlinear Physics — 5 citations into it
Whom Vladimir Gligorijevic trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Pascal Vincent
- Yoshua Bengio
- Amos Bairoch
- Geoffrey E. Hinton
- Hugo Larochelle
- Albert-László Barabási
- Diederik P. Kingma
- Yann LeCun
- Alex Krizhevsky
- Hsuan-Tien Lin
Who trusts Vladimir Gligorijevic overall
Papers from Vladimir Gligorijevic's perspective
Ordered by how strongly the researchers Vladimir Gligorijevic trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- deepNF: deep network fusion for protein function prediction (2017) — 210 citations
- Structure-based protein function prediction using graph convolutional networks (2021) — 926 citations
- The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
- Function-guided protein design by deep manifold sampling (2021) — 35 citations