Matthew F. Dixon

Finance · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Finance — 20 citations into it
• Management Science and Operations Research — 18 citations into it; #1,587 of 11,946 by trust here, #4,267 by citations
• Economics and Econometrics — 17 citations into it; #7,003 of 23,481 by trust here, #17,969 by citations
• Artificial Intelligence — 13 citations into it; #11,680 of 35,345 by trust here, #21,878 by citations
• Statistics and Probability — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Optimization & Training — 6 citations into it

Whom Matthew F. Dixon trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Graves
2. Jerome H. Friedman
3. Xavier Glorot
4. Eric R. Ziegel
5. Justin A. Sirignano
6. Jeffrey L. Elman
7. Alec N. Kercheval
8. Trevor J. Hastie
9. Yoshua Bengio
10. Noah R. Simon

Who trusts Matthew F. Dixon overall

1. A. Ozbayoglu
2. Justin A. Sirignano
3. Omer Berat Sezer
4. M. Ugur Gudelek
5. Avraam Tsantekidis
6. Sid Ghoshal
7. Rama Cont
8. Luigi Troiano
9. Stephen J. Roberts
10. Fuli Feng

Papers from Matthew F. Dixon's perspective

Ordered by how strongly the researchers Matthew F. Dixon trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Sequence classification of the limit order book using recurrent neural networks (2017) — 92 citations
• Classification-based financial markets prediction using deep neural networks (2016) — 199 citations
• A High Frequency Trade Execution Model for Supervised Learning (2017) — 22 citations