Sabato Marco Siniscalchi

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 10 papers; trusts 106 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #7,587 of 35,345 by trust here, #9,760 by citations
• Signal Processing — 75 citations into it; #1,223 of 7,572 by trust here, #1,219 by citations
• Natural Language Processing — 47 citations into it
• Speech & Audio — 40 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Model Architectures — 20 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Sabato Marco Siniscalchi trusts overall

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

1. Geoffrey E. Hinton
2. Dong Yu
3. Li Deng
4. George E. Dahl
5. Abdel-rahman Mohamed
6. Yoshua Bengio
7. Hervé Bourlard
8. Ruslan Salakhutdinov
9. Frank Torsten Bernd Seide
10. Radford M. Neal

Who trusts Sabato Marco Siniscalchi overall

1. Li Deng
2. Zhen Huang
3. Dong Yu
4. Guangsen Wang
5. Jinyu Li
6. Khe Chai Sim
7. Chin-Hui Lee
8. Saptarshi Sengupta
9. Ossama Abdel-Hamid
10. Zhong Meng

Papers from Sabato Marco Siniscalchi's perspective

Ordered by how strongly the researchers Sabato Marco Siniscalchi 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Boosting attribute and phone estimation accuracies with deep neural networks for detection-based speech recognition (2012) — 76 citations
• Rapid adaptation for deep neural networks through multi-task learning (2015) — 69 citations
• Speech Recognition Using Long-Span Temporal Patterns in a Deep Network Model (2013) — 33 citations
• Exploiting deep neural networks for detection-based speech recognition (2013) — 128 citations
• Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems (2013) — 79 citations
• Maximum a posteriori adaptation of network parameters in deep models (2015) — 54 citations
• Feature space maximum a posteriori linear regression for adaptation of deep neural networks (2014) — 21 citations
• On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression (2020) — 299 citations
• Hierarchical Bayesian combination of plug-in maximum a posteriori decoders in deep neural networks-based speech recognition and speaker adaptation (2017) — 13 citations
• A Bottom-Up Modular Search Approach to Large Vocabulary Continuous Speech Recognition (2013) — 21 citations