Jian-Guang Lou
Artificial Intelligence · 2009–2023
A researcher in the Science corpus: 18 papers; trusts 381 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 485 citations into it; #18,076 of 35,345 by trust here, #5,924 by citations
- Natural Language Processing — 311 citations into it; #3,515 of 5,688 by trust here, #1,891 by citations
- Computer Vision and Pattern Recognition — 152 citations into it
- Large Language Models — 126 citations into it
- Information Systems — 112 citations into it; #1,976 of 8,771 by trust here, #647 by citations
- Prompting & Reasoning — 89 citations into it
- Model Architectures — 36 citations into it
- Software — 36 citations into it; #310 of 1,227 by trust here, #195 by citations
Whom Jian-Guang Lou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Oriol Vinyals
- Yann LeCun
- Alex Graves
- Kaiming He
- Sepp Hochreiter
Who trusts Jian-Guang Lou overall
- Min Du
- Shilin He
- Feifei Li
- Qingwei Lin
- Hongyu Zhang
- Van-Hoang Le
- Jieming Zhu
- Pinjia He
- Vijayaraghavan Murali
- Min Du
Papers from Jian-Guang Lou's perspective
Ordered by how strongly the researchers Jian-Guang Lou trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- On the Advance of Making Language Models Better Reasoners (2022) — 162 citations
- Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (2019) — 493 citations
- TAPEX: Table Pre-training via Learning a Neural SQL Executor (2021) — 368 citations
- Benchmarking Meaning Representations in Neural Semantic Parsing (2020) — 29 citations
- Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models (2022) — 47 citations
- WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (2023) — 752 citations
- You Impress Me: Dialogue Generation via Mutual Persona Perception (2020) — 187 citations
- λOpt: Learn to Regularize Recommender Models in Finer Levels (2019) — 47 citations
- RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023) — 516 citations
- Reasoning Like Program Executors (2022) — 61 citations
- CodeT: Code Generation with Generated Tests (2022) — 560 citations
- Making Language Models Better Reasoners with Step-Aware Verifier (2022) — 328 citations
- ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021) — 83 citations
- Compositional Generalization by Learning Analytical Expressions (2020) — 78 citations
- Robust log-based anomaly detection on unstable log data (2019) — 766 citations
- Log Clustering Based Problem Identification for Online Service Systems (2016) — 534 citations
- Mining Invariants from Console Logs for System Problem Detection (2010) — 395 citations
- Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis (2009) — 604 citations