Li-Chia Yang
Computer Vision and Pattern Recognition · 2017
A researcher in the Science corpus: 3 papers; trusts 180 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 53 citations into it; #7,868 of 21,575 by trust here, #8,952 by citations
- Generative Models — 43 citations into it; #740 of 1,956 by trust here, #1,021 by citations
- Signal Processing — 33 citations into it; #2,159 of 7,572 by trust here, #1,857 by citations
- Artificial Intelligence — 21 citations into it
- Natural Language Processing — 9 citations into it
- Cognitive Neuroscience — 7 citations into it
- Multimodal Learning — 6 citations into it; #419 of 1,211 by trust here, #722 by citations
- Model Architectures — 5 citations into it
Whom Li-Chia Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Olof Mogren
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Graves
- Aäron van den Oord
- Alex Krizhevsky
- Gaëtan Hadjeres
- Hang Chu
- Ilya Sutskever
Who trusts Li-Chia Yang overall
- Yu-Siang Huang
- Yi-Hsuan Yang
- Hao-Wen Dong
- Divya Saxena
- Gino Brunner
- Jean-Pierre Briot
- Wen-Yi Hsiao
- Jian Wu
- Prafulla Dhariwal
- Yukiya Hono
Papers from Li-Chia Yang's perspective
Ordered by how strongly the researchers Li-Chia Yang 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- GENERATIVE ADVERSARIAL NETS
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment (2017) — 643 citations
- MidiNet: A Convolutional Generative Adversarial Network for Symbolic-Domain Music Generation (2017) — 509 citations
- MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks (2017) — 38 citations