Antonio Bonafonte

Artificial Intelligence · 2001–2023

A researcher in the Science corpus: 7 papers; trusts 102 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #4,418 of 35,345 by trust here, #5,834 by citations
• Signal Processing — 46 citations into it; #572 of 7,572 by trust here, #514 by citations
• Speech & Audio — 33 citations into it; #188 of 1,076 by trust here, #266 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #12,269 of 21,575 by trust here, #12,992 by citations
• Model Architectures — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it; #2,256 of 3,145 by trust here, #1,913 by citations
• Generative Models — 12 citations into it; #382 of 1,956 by trust here, #644 by citations
• Optimization & Training — 10 citations into it

Whom Antonio Bonafonte trusts overall

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

1. Sepp Hochreiter
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Kaiming He
5. Yariv Ephraim
6. Diederik P. Kingma
7. Li Deng
8. Alex Krizhevsky
9. J. Urgen Schmidhuber
10. Dong Yu

Who trusts Antonio Bonafonte overall

1. Ashutosh Pandey
2. Chris Donahue
3. Igor Jauk
4. Mira Lilleholt Vik
5. Deliang Wang
6. Yi Luo
7. Yu-An Chung
8. Szu-Wei Fu
9. Yuichiro Koyama
10. Craig Macartney

Papers from Antonio Bonafonte's perspective

Ordered by how strongly the researchers Antonio Bonafonte 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks (2019) — 259 citations
• SEGAN: Speech Enhancement Generative Adversarial Network (2017) — 1,295 citations
• Controllable Emphasis with zero data for text-to-speech (2023) — 6 citations
• Distribution Augmentation for Low-Resource Expressive Text-To-Speech (2022) — 10 citations
• Multi-output RNN-LSTM for multiple speaker speech synthesis with α-interpolation model (2016) — 12 citations
• Prosody Generation for Speech-to-Speech Translation (2006) — 62 citations
• Speech emotion recognition using hidden Markov models (2001) — 830 citations