Atticus Geiger

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 90 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 176 citations into it
• Natural Language Processing — 145 citations into it; #3,530 of 5,688 by trust here, #2,045 by citations
• Evaluation & Benchmarks — 57 citations into it; #2,016 of 3,147 by trust here, #1,362 by citations
• Computer Vision and Pattern Recognition — 45 citations into it
• Model Architectures — 18 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Information Systems — 9 citations into it
• Causal Inference & Discovery — 6 citations into it

Whom Atticus Geiger trusts overall

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

1. Bill MacCartney
2. Diederik P. Kingma
3. Christopher D. Manning
4. Geoffrey E. Hinton
5. Samuel R. Bowman
6. Yoshua Bengio
7. Sepp Hochreiter
8. Richard Socher
9. Jimmy Ba
10. Alex Krizhevsky

Who trusts Atticus Geiger overall

1. Douwe Kiela
2. Adina Williams
3. Max Bartolo
4. Tristan Thrush

Papers from Atticus Geiger's perspective

Ordered by how strongly the researchers Atticus Geiger 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Dynabench: Rethinking Benchmarking in NLP (2021) — 580 citations
• Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences (2018) — 25 citations
• Recursive Routing Networks: Learning to Compose Modules for Language Understanding (2019) — 29 citations
• Causal Abstractions of Neural Networks (2021) — 428 citations
• DynaSent: A Dynamic Benchmark for Sentiment Analysis (2020) — 87 citations