Minping Jia

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it
• Control and Systems Engineering — 27 citations into it; #6,130 of 8,257 by trust here, #6,417 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Mechanical Engineering — 17 citations into it; #5,414 of 8,300 by trust here, #7,458 by citations
• Model Architectures — 17 citations into it
• Mechanics of Materials — 8 citations into it; #4,437 of 6,291 by trust here, #5,405 by citations
• Optimization & Training — 5 citations into it

Whom Minping Jia trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Jimmy Ba
4. Kaiming He
5. Yoshua Bengio
6. Sepp Hochreiter
7. Alex Krizhevsky
8. Christian Szegedy
9. David E. Rumelhart
10. J. Urgen Schmidhuber

Who trusts Minping Jia overall

1. Yifei Ding

Papers from Minping Jia's perspective

Ordered by how strongly the researchers Minping Jia 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings (2021) — 447 citations
• A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings (2021) — 332 citations
• A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing (2018) — 450 citations