Wenwu Wang
Computer Vision and Pattern Recognition · with Liu · 2017–2023
A researcher in the Science corpus: 7 papers; trusts 213 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 147 citations into it
- Signal Processing — 137 citations into it; #5,467 of 7,572 by trust here, #1,255 by citations
- Artificial Intelligence — 134 citations into it; #31,732 of 35,345 by trust here, #10,207 by citations
- Generative Models — 52 citations into it
- Evaluation & Benchmarks — 36 citations into it
- Music — 18 citations into it
- Optimization & Training — 18 citations into it
- Statistical and Nonlinear Physics — 17 citations into it
Whom Wenwu Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Karen Simonyan
- Ilya Sutskever
- Jimmy Ba
- Yann LeCun
- Vinod Nair
Who trusts Wenwu Wang overall
Papers from Wenwu Wang's perspective
Ordered by how strongly the researchers Wenwu Wang 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)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Audio Set Classification with Attention Model: A Probabilistic Perspective (2017) — 107 citations
- PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition (2019) — 1,568 citations
- AudioLDM: Text-to-Audio Generation with Latent Diffusion Models (2023) — 854 citations
- Separate Anything You Describe (2023) — 87 citations
- AudioLDM 2: Learning Holistic Audio Generation With Self-Supervised Pretraining (2023) — 512 citations
- WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research (2023) — 418 citations
- Learning With Out-of-Distribution Data for Audio Classification (2020) — 19 citations