David Leo Wright Hall

Artificial Intelligence · 2008–2023

A researcher in the Science corpus: 7 papers; trusts 50 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 110 citations into it; #5,924 of 35,345 by trust here, #8,283 by citations
• Natural Language Processing — 55 citations into it; #2,579 of 5,688 by trust here, #3,574 by citations
• Optimization & Training — 36 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it; #13,544 of 21,575 by trust here, #11,950 by citations
• Evaluation & Benchmarks — 11 citations into it
• Information Retrieval & Search — 10 citations into it

Whom David Leo Wright Hall trusts overall

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

1. David M. Blei
2. Slav Petrov
3. T. Griffiths
4. Eugene Garfield
5. Daniel Klein
6. Diederik P. Kingma
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. Michael I. Jordan
10. Kaiming He

Who trusts David Leo Wright Hall overall

1. Jey Han Lau
2. Jason Chuang
3. Adriana Kovashka
4. Shaheen Syed
5. Mark Anderson
6. Adam Vogel
7. Greg Durrett
8. Christopher D. Manning
9. Dragomir R. Radev
10. Daniel Ramage

Papers from David Leo Wright Hall's perspective

Ordered by how strongly the researchers David Leo Wright Hall trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora (2009) — 1,475 citations
• Task-Oriented Dialogue as Dataflow Synthesis (2020) — 176 citations
• Sparser, Better, Faster GPU Parsing (2014) — 21 citations
• Studying the History of Ideas Using Topic Models (2008) — 515 citations
• A Multi-Teraflop Constituency Parser using GPUs (2013) — 22 citations
• Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training (2023) — 300 citations
• Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2022) — 389 citations