Hirokazu Kameoka
Artificial Intelligence · 2009–2022
A researcher in the Science corpus: 10 papers; trusts 470 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 158 citations into it; #10,057 of 35,345 by trust here, #14,438 by citations
- Signal Processing — 124 citations into it; #1,323 of 7,572 by trust here, #1,904 by citations
- Computer Vision and Pattern Recognition — 79 citations into it
- Speech & Audio — 64 citations into it; #946 of 1,076 by trust here, #797 by citations
- Generative Models — 54 citations into it; #1,757 of 1,956 by trust here, #1,549 by citations
- Natural Language Processing — 48 citations into it
- Model Architectures — 31 citations into it
- Representation & Self-Supervised Learning — 17 citations into it
Whom Hirokazu Kameoka trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Jimmy Ba
- Alex Krizhevsky
- Aäron van den Oord
- Alex Graves
- Yann LeCun
Who trusts Hirokazu Kameoka overall
- Kaizhi Qian
- Berrak Sisman
- Hubert Siuzdak
- Nasser Mohammadiha
- Momina Masood
- Hieu-Thi Luong
- Junichi Yamagishi
- Takuhiro Kaneko
- Rita Singh
- Songxiang Liu
Papers from Hirokazu Kameoka's perspective
Ordered by how strongly the researchers Hirokazu Kameoka 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)
- Long Short-Term Memory
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Seeing through Sounds: Predicting Visual Semantic Segmentation Results from Multichannel Audio Signals (2019) — 16 citations
- CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks (2018) — 325 citations
- Many-to-Many Voice Transformer Network (2020) — 33 citations
- Pretraining Techniques for Sequence-to-Sequence Voice Conversion (2020) — 49 citations
- ISTFTNET: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform (2022) — 96 citations
- StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks (2018) — 419 citations
- Generative adversarial network-based approach to signal reconstruction from magnitude spectrogram (2018) — 33 citations
- Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization (2016) — 336 citations
- Generative adversarial network-based postfilter for statistical parametric speech synthesis (2016) — 128 citations
- Robust speech dereverberation based on non-negativity and sparse nature of speech spectrograms (2009) — 87 citations