Zachary M. Ziegler

Artificial Intelligence · 2019

A researcher in the Science corpus: 4 papers; trusts 299 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #8,944 of 35,345 by trust here, #15,236 by citations
• Natural Language Processing — 54 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it; #14,978 of 21,575 by trust here, #15,162 by citations
• Generative Models — 20 citations into it
• Model Architectures — 13 citations into it; #1,624 of 2,278 by trust here, #1,976 by citations
• Large Language Models — 10 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Zachary M. Ziegler trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Tomas Mikolov
7. Oriol Vinyals
8. Alex Graves
9. Yann LeCun
10. Kaiming He

Who trusts Zachary M. Ziegler overall

1. Emiel Hoogeboom
2. Lei Yu
3. John Wieting
4. Aaron Lou
5. Ivan Kobyzev
6. Eyal Orbach
7. Michael Sollami
8. Dustin Tran
9. Jacob Austin
10. Didrik Nielsen

Papers from Zachary M. Ziegler's perspective

Ordered by how strongly the researchers Zachary M. Ziegler 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Latent Normalizing Flows for Discrete Sequences (2019) — 136 citations
• Encoder-Agnostic Adaptation for Conditional Language Generation (2019) — 60 citations
• Generating Abstractive Summaries with Finetuned Language Models (2019) — 23 citations
• Mechanisms of spatiotemporal mode-locking (2019) — 156 citations