Daniel Simig

Artificial Intelligence · 2021–2023

A researcher in the Science corpus: 5 papers; trusts 64 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 248 citations into it
• Natural Language Processing — 127 citations into it
• Computer Vision and Pattern Recognition — 103 citations into it
• Optimization & Training — 11 citations into it

Whom Daniel Simig trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Alec Radford
7. Kaiming He
8. Yann LeCun
9. Alex Graves
10. Quoc V. Le

Who trusts Daniel Simig overall

1. Luke S. Zettlemoyer

Papers from Daniel Simig's perspective

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

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

Their papers

• OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization (2022) — 307 citations
• Few-shot Learning with Multilingual Generative Language Models (2021) — 384 citations
• SemDeDup: Data-efficient learning at web-scale through semantic deduplication (2023) — 307 citations
• MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers (2023) — 177 citations
• OPT: Open Pre-trained Transformer Language Models (2022) — 4,844 citations