Wei Wang
Artificial Intelligence · International Conference on Information Networking · 2017–2018
A researcher in the Science corpus: 3 papers; trusts 5 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 26 citations into it; #20,905 of 35,345 by trust here, #15,004 by citations
- Computer Networks and Communications — 16 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Natural Language Processing — 6 citations into it
Whom Wei Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Yoshua Bengio
- Geoffrey E. Hinton
- Yoon Kim
- Sepp Hochreiter
- L. Eon Bottou
- Ronan Collobert
- Alex Krizhevsky
- J. Urgen Schmidhuber
- L. V. D. van der Maaten
Who trusts Wei Wang overall
- Shahbaz Rezaei
- Chaoyun Zhang
- Daniel S. Berman
- Xin Liu
- Hongyu Liu
- Paul Patras
- Giuseppe Aceto
- Kamran Shaukat
- Anna L. Buczak
- Mohammad Lotfollahi
Papers from Wei Wang's perspective
Ordered by how strongly the researchers Wei Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection (2018) — 571 citations
- Malware traffic classification using convolutional neural network for representation learning (2017) — 924 citations
- End-to-end encrypted traffic classification with one-dimensional convolution neural networks (2017) — 825 citations