Jingshan Tang

Artificial Intelligence · 2022

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it
• Computer Vision and Pattern Recognition — 43 citations into it
• Representation & Self-Supervised Learning — 19 citations into it

Whom Jingshan Tang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He

Papers from Jingshan Tang's perspective

Ordered by how strongly the researchers Jingshan Tang 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. Learning Multiple Layers of Features from Tiny Images
3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Their papers

• MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning (2022) — 188 citations
• Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay (2022) — 137 citations