Yoshinori Shiga
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 4 papers; trusts 130 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 93 citations into it; #11,935 of 35,345 by trust here, #18,586 by citations
- Signal Processing — 74 citations into it; #2,210 of 7,572 by trust here, #3,198 by citations
- Speech & Audio — 54 citations into it; #935 of 1,076 by trust here, #1,043 by citations
- Generative Models — 36 citations into it; #1,715 of 1,956 by trust here, #1,926 by citations
- Natural Language Processing — 34 citations into it
- Computer Vision and Pattern Recognition — 21 citations into it
- Probabilistic & Bayesian Methods — 5 citations into it
- Statistical and Nonlinear Physics — 5 citations into it
Whom Yoshinori Shiga trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Aäron van den Oord
- Geoffrey E. Hinton
- Yoshua Bengio
- Wei Ping
- Alex Graves
- Keiichi Tokuda
- Alex Krizhevsky
- Karen Simonyan
- Danilo Jimenez Rezende
Who trusts Yoshinori Shiga overall
Papers from Yoshinori Shiga's perspective
Ordered by how strongly the researchers Yoshinori Shiga trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Noise Level Limited Sub-Modeling for Diffusion Probabilistic Vocoders (2021) — 11 citations
- Tacotron-Based Acoustic Model Using Phoneme Alignment for Practical Neural Text-to-Speech Systems (2019) — 26 citations
- An Investigation of Subband Wavenet Vocoder Covering Entire Audible Frequency Range with Limited Acoustic Features (2018) — 24 citations
- Improving FFTNet Vocoder with Noise Shaping and Subband Approaches (2018) — 17 citations