Dan Iter

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 6 papers; trusts 59 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it
• Computer Vision and Pattern Recognition — 72 citations into it
• Natural Language Processing — 70 citations into it
• Evaluation & Benchmarks — 50 citations into it
• Molecular Biology — 7 citations into it

Whom Dan Iter trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Ilya Sutskever
6. Yann LeCun
7. Kaiming He
8. Gautier Izacard
9. Sepp Hochreiter
10. Alex Graves

Who trusts Dan Iter overall

1. Yu Wang
2. Alex Troy Mallen

Papers from Dan Iter's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment (2023) — 2,735 citations
• Generate rather than Retrieve: Large Language Models are Strong Context Generators (2022) — 431 citations
• Automatic Prompt Optimization with "Gradient Descent" and Beam Search (2023) — 730 citations
• Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data (2016) — 26 citations
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 citations
• Socratic Learning (2016)