Mark Dras

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 4 papers; trusts 68 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Natural Language Processing — 21 citations into it; #2,755 of 5,688 by trust here, #5,296 by citations
• Evaluation & Benchmarks — 12 citations into it
• Management Science and Operations Research — 9 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Economics and Econometrics — 6 citations into it
• Finance — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Mark Dras trusts overall

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

1. Philipp Koehn
2. Chris Callison-Burch
3. Mark Hopkins
4. Michael Collins
5. Diederik P. Kingma
6. Yoshua Bengio
7. Sepp Hochreiter
8. Geoffrey E. Hinton
9. Andrew L. Maas
10. Xiao Ding

Who trusts Mark Dras overall

1. Hua He
2. A. Ozbayoglu
3. Omer Berat Sezer
4. M. Ugur Gudelek
5. Ze Hu
6. Sebastian Gehrmann
7. Wenpeng Yin
8. Jonathan Berant
9. Noah A. Smith
10. Dipanjan Das

Papers from Mark Dras's perspective

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

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Predicting accuracy on large datasets from smaller pilot data (2018) — 45 citations
• Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading (2017) — 48 citations
• Squibs: Evaluating Human Pairwise Preference Judgments (2015) — 8 citations
• Using Dependency-Based Features to Take the ’Para-farce’ out of Paraphrase (2006) — 178 citations