Jindong Wang

Artificial Intelligence · 2017–2026

A researcher in the Science corpus: 18 papers; trusts 869 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 656 citations into it; #14,570 of 35,345 by trust here, #3,673 by citations
• Computer Vision and Pattern Recognition — 358 citations into it; #12,569 of 21,575 by trust here, #4,438 by citations
• Representation & Self-Supervised Learning — 213 citations into it
• Natural Language Processing — 89 citations into it
• Signal Processing — 63 citations into it
• Safety, Robustness & Interpretability — 56 citations into it
• Optimization & Training — 40 citations into it
• Cancer Research — 32 citations into it

Whom Jindong Wang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Kaiming He
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Sinno Jialin Pan
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Jindong Wang overall

1. Yiqiang Chen
2. Chaohui Yu
3. Yuxiang Zhang
4. Sreenivasan Ramasamy Ramamurthy
5. Fuzhen Zhuang
6. Yuntao Du
7. Sakorn Mekruksavanich
8. Wouter M. Kouw
9. Yongchun Zhu
10. Elangovan Ramanujam

Papers from Jindong Wang's perspective

Ordered by how strongly the researchers Jindong Wang 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. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Visual Domain Adaptation with Manifold Embedded Distribution Alignment (2018) — 651 citations
• Balanced Distribution Adaptation for Transfer Learning (2017) — 540 citations
• A Survey on Evaluation of Large Language Models (2023) — 3,582 citations
• Transfer Learning with Dynamic Adversarial Adaptation Network (2019) — 388 citations
• FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare (2019) — 933 citations
• Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation (2020) — 161 citations
• Generalizing to Unseen Domains: A Survey on Domain Generalization (2021) — 1,790 citations
• PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization (2023) — 400 citations
• Stratified Transfer Learning for Cross-domain Activity Recognition (2017) — 244 citations
• UniSD: Towards a Unified Self-Distillation Framework for Large Language Models (2026)
• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective (2023) — 310 citations
• PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts (2023) — 312 citations
• FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning (2022) — 477 citations
• FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling (2021) — 1,316 citations
• AdaRNN: Adaptive Learning and Forecasting of Time Series (2021) — 371 citations
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021) — 342 citations
• Deep Subdomain Adaptation Network for Image Classification (2020) — 1,091 citations
• Deep Learning for Sensor-based Activity Recognition: A Survey (2017) — 1,854 citations