Titouan Parcollet

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 66 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #13,990 of 35,345 by trust here, #12,244 by citations
• Signal Processing — 84 citations into it; #2,710 of 7,572 by trust here, #2,052 by citations
• Speech & Audio — 71 citations into it; #514 of 1,076 by trust here, #654 by citations
• Computer Vision and Pattern Recognition — 39 citations into it
• Model Architectures — 33 citations into it
• Natural Language Processing — 30 citations into it
• Optimization & Training — 28 citations into it
• Evaluation & Benchmarks — 25 citations into it

Whom Titouan Parcollet trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Graves
4. Mirco Ravanelli
5. Diederik P. Kingma
6. Kaiming He
7. Xavier Glorot
8. Ilya Sutskever
9. Alex Krizhevsky
10. Sepp Hochreiter

Who trusts Titouan Parcollet overall

1. Mirco Ravanelli
2. Yi Tay
3. Aston Zhang
4. Shuai Zhang
5. Yoshua Bengio
6. Hervé Bredin
7. Tuan Le
8. Pierre-Amaury Grumiaux
9. Santiago Pascual
10. Anh Tuan Luu

Papers from Titouan Parcollet's perspective

Ordered by how strongly the researchers Titouan Parcollet 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. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• The Pytorch-kaldi Speech Recognition Toolkit (2018) — 241 citations
• Quaternion Recurrent Neural Networks (2018) — 153 citations
• Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition (2018) — 116 citations
• SpeechBrain: A General-Purpose Speech Toolkit (2021) — 999 citations
• Flower: A Friendly Federated Learning Research Framework (2020) — 1,331 citations
• A first look into the carbon footprint of federated learning (2020) — 122 citations