Mark P. Waller
Computational Chemistry & Drug Discovery · 2016–2017
A researcher in the Science corpus: 9 papers; trusts 67 researchers and is trusted by 77 researchers here, as inferred from the citation graph.
Subfields
- Computational Chemistry & Drug Discovery — 118 citations into it; #21 of 425 by trust here, #51 by citations
- Computational Theory and Mathematics — 84 citations into it; #381 of 12,182 by trust here, #200 by citations
- Molecular Biology — 71 citations into it; #679 of 17,773 by trust here, #3,275 by citations
- Materials Chemistry — 69 citations into it; #205 of 11,513 by trust here, #2,018 by citations
- Artificial Intelligence — 47 citations into it; #19,324 of 35,345 by trust here, #7,310 by citations
- Organic Chemistry — 24 citations into it
- Spectroscopy — 23 citations into it
- Computer Vision and Pattern Recognition — 19 citations into it
Whom Mark P. Waller trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Matthew A. Kayala
- Keith J. Holyoak
- Marwin H. S. Segler
- Gonçalo V. S. M. Carrera
- Alex Graves
- R. Grosse-Kunstleve
- Sepp Hochreiter
- J. Urgen Schmidhuber
- João Aires-de-Sousa
- Djork-Arné Clevert
Who trusts Mark P. Waller overall
- Philippe Schwaller
- Marwin H. S. Segler
- Connor W. Coley
- Tânia F Cova
- Philipp Seidl
- Miha Škalič
- Sergey I. Nikolenko
- Lauv Patel
- Jiaxuan You
- Rohit Batra
Papers from Mark P. Waller's perspective
Ordered by how strongly the researchers Mark P. Waller trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- 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)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Planning chemical syntheses with deep neural networks and symbolic AI (2017) — 1,608 citations
- Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017) — 1,386 citations
- Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017) — 98 citations
- Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. (2017) — 512 citations
- Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies (2017) — 35 citations
- Solving the scalability issue in quantum-based refinement: Q|R#1. (2017) — 15 citations
- Q|R: quantum-based refinement. (2017) — 19 citations
- Modelling Chemical Reasoning to Predict and Invent Reactions Marwin (2016)
- Modelling Chemical Reasoning to Predict Reactions (2016) — 128 citations