Roger G. Melko
Atomic and Molecular Physics, and Optics · 2001–2018
A researcher in the Science corpus: 8 papers; trusts 17 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Atomic and Molecular Physics, and Optics — 60 citations into it; #825 of 7,135 by trust here, #1,177 by citations
- Artificial Intelligence — 54 citations into it
- Condensed Matter Physics — 23 citations into it
- Materials Chemistry — 17 citations into it; #1,898 of 11,513 by trust here, #5,818 by citations
- Statistical and Nonlinear Physics — 15 citations into it
- Probabilistic & Bayesian Methods — 13 citations into it
- Computer Vision and Pattern Recognition — 12 citations into it
Whom Roger G. Melko trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Giuseppe Carleo
- Ruslan Salakhutdinov
- Radford M. Neal
- Michael Levin
- Yann LeCun
- Yoshua Bengio
- Matthias Troyer
- Matthew Fisher
Who trusts Roger G. Melko overall
- Bernhard Keimer
- Wei Ma
- Giuseppe Carleo
- Dong-Ling Deng
- H. C. Nguyen
- Vedran Dunjko
- Jacob D. Biamonte
- Hsin-Yuan Huang
- J. Moore
Papers from Roger G. Melko's perspective
Ordered by how strongly the researchers Roger G. Melko trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- This PDF file includes: Materials and Methods
Their papers
- Machine learning phases of matter (2016) — 1,406 citations
- Machine Learning Phases of Strongly Correlated Fermions (2016) — 310 citations
- Machine learning quantum phases of matter beyond the fermion sign problem (2016) — 310 citations
- Reconstructing quantum states with generative models (2018) — 341 citations
- Neural-network quantum state tomography (2017) — 901 citations
- Quantum Boltzmann Machine (2016) — 599 citations
- Topological entanglement entropy of a Bose-Hubbard spin liquid (2011) — 204 citations
- Magnetic anisotropy of the spin-ice compound Dy2Ti2O7 (2001) — 138 citations