Vedran Dunjko
Artificial Intelligence · 2016–2023
A researcher in the Science corpus: 4 papers; trusts 57 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 160 citations into it; #24,300 of 35,345 by trust here, #14,438 by citations
- Atomic and Molecular Physics, and Optics — 93 citations into it; #4,095 of 7,135 by trust here, #5,903 by citations
- Computational Theory and Mathematics — 45 citations into it
- Reinforcement Learning — 22 citations into it
- Materials Chemistry — 18 citations into it
- Statistical Learning Theory — 14 citations into it
- Statistical and Nonlinear Physics — 14 citations into it
- Model Architectures — 13 citations into it
Whom Vedran Dunjko trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Seth Lloyd
- Miklos Santha
- Kurt Hornik
- Lei Wang
- Frank Verstraete
- Edward Farhi
- Richard S. Sutton
- Andrew M. Childs
- Christopher J. C. H. Watkins
- John J. Hopfield
Who trusts Vedran Dunjko overall
Papers from Vedran Dunjko's perspective
Ordered by how strongly the researchers Vedran Dunjko 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
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Challenges and opportunities in quantum optimization (2023) — 341 citations
- Machine learning & artificial intelligence in the quantum domain: a review of recent progress (2018) — 684 citations
- Machine learning \& artificial intelligence in the quantum domain (2017) — 359 citations
- Quantum-enhanced machine learning (2016) — 450 citations