Hakan Inan

Artificial Intelligence · with Fu · 2020–2024

A researcher in the Science corpus: 5 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 177 citations into it
• Natural Language Processing — 91 citations into it
• Information Systems — 18 citations into it

Whom Hakan Inan trusts overall

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

1. Mihir Kale
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alyssa Lees
6. Todor Markov
7. Ilya Sutskever
8. Alex Krizhevsky

Who trusts Hakan Inan overall

1. Seungju Han

Papers from Hakan Inan's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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 classification with deep convolutional neural networks
8. Language Models are Unsupervised Multitask Learners
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Bleu: a Method for Automatic Evaluation of Machine Translation

Their papers

• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations (2023) — 1,166 citations
• Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task (2020)
• Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020) — 10 citations