Yingtao Tian
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 6 papers; trusts 433 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 73 citations into it; #13,696 of 21,575 by trust here, #8,122 by citations
- Artificial Intelligence — 68 citations into it; #16,400 of 35,345 by trust here, #10,798 by citations
- Generative Models — 67 citations into it; #1,268 of 1,956 by trust here, #949 by citations
- Natural Language Processing — 18 citations into it
- Optimization & Training — 15 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
- Statistical and Nonlinear Physics — 13 citations into it
- Signal Processing — 10 citations into it
Whom Yingtao Tian trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Jimmy Ba
- Kaiming He
- Yann LeCun
- Sepp Hochreiter
- J. Urgen Schmidhuber
Who trusts Yingtao Tian overall
- Zequn Sun
- Wengong Jin
- Bidisha Samanta
- Jiaxuan You
- Wei Hu
- Benjamín Sánchez-Lengeling
- Chengxi Zang
- Tianfan Fu
- Lingbing Guo
- Bayu Distiawan Trisedya
Papers from Yingtao Tian's perspective
Ordered by how strongly the researchers Yingtao Tian 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Syntax-Directed Variational Autoencoder for Structured Data (2018) — 358 citations
- Towards the Automatic Anime Characters Creation with Generative Adversarial Networks (2017) — 202 citations
- Latent Translation: Crossing Modalities by Bridging Generative Models (2019) — 18 citations
- Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment (2016) — 626 citations
- Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts (2021) — 63 citations
- Towards the High-quality Anime Characters Generation with Generative Adversarial Networks (2017)