Takuhiro Kaneko

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 616 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #10,056 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #16,989 of 21,575 by trust here, #17,258 by citations
• Signal Processing — 80 citations into it; #1,600 of 7,572 by trust here, #1,820 by citations
• Generative Models — 72 citations into it; #1,362 of 1,956 by trust here, #1,315 by citations
• Speech & Audio — 45 citations into it; #945 of 1,076 by trust here, #821 by citations
• Natural Language Processing — 30 citations into it
• Model Architectures — 28 citations into it
• Optimization & Training — 12 citations into it

Whom Takuhiro Kaneko trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Yoshua Bengio
6. Jimmy Ba
7. Yann LeCun
8. Ilya Sutskever
9. Alex Graves
10. Karen Simonyan

Who trusts Takuhiro Kaneko overall

1. Berrak Sisman
2. Hubert Siuzdak
3. Hirokazu Kameoka
4. Andros Tjandra
5. Momina Masood
6. Jaeseong You
7. Junichi Yamagishi
8. Ke Li
9. Andreas Triantafyllopoulos
10. Ayush Jaiswal

Papers from Takuhiro Kaneko's perspective

Ordered by how strongly the researchers Takuhiro Kaneko trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Generative Attribute Controller with Conditional Filtered Generative Adversarial Networks (2017) — 95 citations
• CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks (2018) — 325 citations
• Many-to-Many Voice Transformer Network (2020) — 33 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
• Generative adversarial network-based postfilter for statistical parametric speech synthesis (2016) — 128 citations