Xiao Liu
Artificial Intelligence · Annual Meeting of the Association for Computational Linguistics · 2019–2024
A researcher in the Science corpus: 19 papers; trusts 681 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 971 citations into it; #12,908 of 35,345 by trust here, #1,579 by citations
- Computer Vision and Pattern Recognition — 461 citations into it
- Natural Language Processing — 406 citations into it
- Large Language Models — 327 citations into it; #337 of 799 by trust here, #229 by citations
- Evaluation & Benchmarks — 163 citations into it
- Representation & Self-Supervised Learning — 148 citations into it
- Model Architectures — 115 citations into it
- Prompting & Reasoning — 90 citations into it
Whom Xiao Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Ilya Sutskever
- Alec Radford
- Yann LeCun
- Alex Graves
- Ruslan Salakhutdinov
Who trusts Xiao Liu overall
- Xu Han
- Jingfeng Yang
- Renrui Zhang
- Shirui Pan
- Hyojin Bahng
- Ming Ding
- Koustava Goswami
- Yuxian Gu
- Xizhou Zhu
- Ning Ding
Papers from Xiao Liu's perspective
Ordered by how strongly the researchers Xiao Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- GLM-130B: An Open Bilingual Pre-trained Model (2022) — 1,288 citations
- P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks (2021) — 1,125 citations
- GPT Understands, Too (2021) — 1,460 citations
- ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation (2023) — 1,142 citations
- Self-Supervised Learning: Generative or Contrastive (2020) — 2,207 citations
- Pre-Trained Models: Past, Present and Future (2021) — 1,095 citations
- Language Models are Open Knowledge Graphs (2020) — 149 citations
- GraphMAE: Self-Supervised Masked Graph Autoencoders (2022) — 977 citations
- GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2021) — 1,956 citations
- WuDaoCorpora: A super large-scale Chinese corpora for pre-training language models (2021) — 131 citations
- AgentTuning: Enabling Generalized Agent Abilities for LLMs (2023) — 347 citations
- AgentBench: Evaluating LLMs as Agents (2023) — 1,063 citations
- OAG: Toward Linking Large-scale Heterogeneous Entity Graphs (2019) — 133 citations
- P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (2022) — 1,117 citations
- ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools (2024) — 1,646 citations
- LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2023) — 1,466 citations
- DeepStruct: Pretraining of Language Models for Structure Prediction (2022) — 98 citations
- OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models (2021) — 15 citations
- All NLP Tasks Are Generation Tasks: A General Pretraining Framework (2021) — 68 citations