Shota Horiguchi

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 243 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #15,674 of 35,345 by trust here, #15,866 by citations
• Signal Processing — 103 citations into it; #3,098 of 7,572 by trust here, #2,525 by citations
• Speech & Audio — 71 citations into it; #744 of 1,076 by trust here, #900 by citations
• Computer Vision and Pattern Recognition — 35 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Model Architectures — 17 citations into it
• Natural Language Processing — 14 citations into it; #4,002 of 5,688 by trust here, #4,682 by citations
• Optimization & Training — 8 citations into it

Whom Shota Horiguchi trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ilya Sutskever
5. Jimmy Ba
6. Yoshua Bengio
7. Yann LeCun
8. Zhuo Chen
9. Yusuke Fujita
10. Dong Yu

Who trusts Shota Horiguchi overall

1. Shinji Watanabe
2. Yusuke Fujita
3. Tae Jin Park
4. Naoyuki Kanda
5. Yawen Xue
6. Grzegorz Rype's'c
7. Sanyuan Chen
8. Leibny Paola García-Perera
9. Lu Yu
10. Hervé Bredin

Papers from Shota Horiguchi's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Going deeper with convolutions

Their papers

• Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features (2017) — 70 citations
• End-to-End Neural Speaker Diarization with Permutation-Free Objectives (2019) — 312 citations
• Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors (2021) — 46 citations
• Encoder-Decoder Based Attractor Calculation for End-to-End Neural Diarization (2021) — 6 citations
• Neural Speaker Diarization with Speaker-Wise Chain Rule (2020) — 51 citations
• End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors (2020) — 230 citations
• Acoustic Modeling for Distant Multi-talker Speech Recognition with Single- and Multi-channel Branches (2019) — 45 citations