Zihan Liu

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 9 papers; trusts 680 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 284 citations into it; #24,003 of 35,345 by trust here, #16,771 by citations
• Natural Language Processing — 195 citations into it; #5,606 of 5,688 by trust here, #3,638 by citations
• Computer Vision and Pattern Recognition — 151 citations into it; #19,798 of 21,575 by trust here, #17,258 by citations
• Large Language Models — 57 citations into it
• Multimodal Learning — 55 citations into it; #1,087 of 1,211 by trust here, #1,104 by citations
• Representation & Self-Supervised Learning — 42 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Model Architectures — 27 citations into it

Whom Zihan Liu trusts overall

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

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

Who trusts Zihan Liu overall

1. Muhammad Abdul-Mageed
2. Eduard H. Hovy
3. Sarah Wiegreffe
4. Genta Indra Winata
5. Andrea Madotto
6. Linting Xue
7. Luca Massarelli
8. Wenliang Dai
9. B. Ermiş
10. Pascale Fung

Papers from Zihan Liu's perspective

Ordered by how strongly the researchers Zihan Liu 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables (2019) — 71 citations
• Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning (2020) — 20 citations
• Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training (2022) — 85 citations
• Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters (2021) — 47 citations
• AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (2021) — 87 citations
• Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization (2021) — 85 citations
• Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems (2020) — 72 citations
• Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems (2020) — 63 citations
• Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring (2019) — 21 citations