Peng Li

Artificial Intelligence · arXiv.org · 2006–2024

A researcher in the Science corpus: 26 papers; trusts 470 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 586 citations into it; #11,678 of 35,345 by trust here, #3,246 by citations
Natural Language Processing — 335 citations into it; #3,846 of 5,688 by trust here, #1,377 by citations
Computer Vision and Pattern Recognition — 232 citations into it; #15,993 of 21,575 by trust here, #4,212 by citations
Large Language Models — 116 citations into it
Computer Networks and Communications — 75 citations into it
Representation & Self-Supervised Learning — 72 citations into it
Evaluation & Benchmarks — 70 citations into it; #2,919 of 3,147 by trust here, #2,442 by citations
Information Systems — 64 citations into it

Whom Peng Li trusts overall

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

Who trusts Peng Li overall

Papers from Peng Li's perspective

Ordered by how strongly the researchers Peng Li 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
Gradient-based learning applied to document recognition
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)
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

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation (2016) — 224 citations
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering (2016) — 104 citations
NumNet: Machine Reading Comprehension with Numerical Reasoning (2019) — 131 citations
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (2019) — 279 citations
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (2019) — 188 citations
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (2019) — 584 citations
Performances improvement of FPGA using novel multilevel hierarchical interconnection structure (2006) — 67 citations
A Survey of Incentive Mechanism Design for Federated Learning (2021) — 351 citations
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security (2024) — 355 citations
Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf (2023) — 303 citations
SeamlessM4T: Massively Multilingual&Multimodal Machine Translation (2023) — 202 citations
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022) — 45 citations
ELLE: Efficient Lifelong Pre-training for Emerging Data (2022) — 92 citations
On Transferability of Prompt Tuning for Natural Language Understanding (2021) — 21 citations
Knowledge Inheritance for Pre-trained Language Models (2021) — 65 citations
Exploring Low-dimensional Intrinsic Task Subspace via Prompt Tuning (2021) — 39 citations
A Learning-Based Incentive Mechanism for Federated Learning (2020) — 534 citations
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020) — 158 citations
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2020) — 143 citations
MAVEN: A Massive General Domain Event Detection Dataset (2020) — 225 citations