Zhifang Sui
Artificial Intelligence · 2016–2024
A researcher in the Science corpus: 19 papers; trusts 422 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 518 citations into it; #17,578 of 35,345 by trust here, #3,487 by citations
- Natural Language Processing — 272 citations into it; #3,747 of 5,688 by trust here, #1,874 by citations
- Computer Vision and Pattern Recognition — 176 citations into it
- Large Language Models — 163 citations into it; #451 of 799 by trust here, #506 by citations
- Prompting & Reasoning — 94 citations into it
- Evaluation & Benchmarks — 82 citations into it
- Model Architectures — 45 citations into it
- Representation & Self-Supervised Learning — 18 citations into it
Whom Zhifang Sui trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Alex Graves
- Jimmy Ba
- Yann LeCun
- Sepp Hochreiter
- Kaiming He
Who trusts Zhifang Sui overall
Papers from Zhifang Sui's perspective
Ordered by how strongly the researchers Zhifang Sui 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Table-to-text Generation by Structure-aware Seq2seq Learning (2017) — 284 citations
- Knowledge Neurons in Pretrained Transformers (2021) — 725 citations
- Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers (2023) — 401 citations
- Order-Planning Neural Text Generation From Structured Data (2017) — 117 citations
- Large Language Models are not Fair Evaluators (2023) — 1,116 citations
- XGPT: Cross-modal Generative Pre-Training for Image Captioning (2020) — 84 citations
- A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (2019) — 188 citations
- A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation (2021) — 203 citations
- A Survey on In-context Learning (2022) — 1,101 citations
- Learning to Control the Fine-grained Sentiment for Story Ending Generation (2019) — 54 citations
- Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation (2022) — 189 citations
- Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024) — 297 citations
- DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024) — 972 citations
- Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2023) — 1,036 citations
- Making Large Language Models Better Reasoners with Alignment (2023) — 91 citations
- A Survey for In-context Learning (2023) — 401 citations
- Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT (2021)
- Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View (2021) — 37 citations
- Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition (2016) — 68 citations