Stéphane Dupont
Artificial Intelligence · 2000–2017
A researcher in the Science corpus: 9 papers; trusts 316 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 54 citations into it; #9,705 of 35,345 by trust here, #15,542 by citations
- Computer Vision and Pattern Recognition — 32 citations into it; #17,707 of 21,575 by trust here, #19,310 by citations
- Natural Language Processing — 29 citations into it; #4,715 of 5,688 by trust here, #5,296 by citations
- Signal Processing — 15 citations into it; #2,045 of 7,572 by trust here, #2,990 by citations
- Model Architectures — 14 citations into it
- Speech & Audio — 14 citations into it
- Experimental and Cognitive Psychology — 12 citations into it
- Evaluation & Benchmarks — 11 citations into it
Whom Stéphane Dupont trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Hynek Hermansky
- Yoshua Bengio
- Kyunghyun Cho
- Geoffrey E. Hinton
- Matthew D. Zeiler
- Ilya Sutskever
- Kaiming He
- Oriol Vinyals
- Dzmitry Bahdanau
- Alex Krizhevsky
Who trusts Stéphane Dupont overall
- Daniel Gatica-Perez
- Pingchuan Ma
- Gerasimos Potamianos
- Stavros Petridis
- Kevin El Haddad
- Brais Martínez
- Chalapathy Neti
- Hao Zhu
- Ara V. Nefian
- Iain McCowan
Papers from Stéphane Dupont's perspective
Ordered by how strongly the researchers Stéphane Dupont trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Audio-Visual Speech Modeling for Continuous Speech Recognition (2000) — 637 citations
- Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation (2017) — 9 citations
- Introducing AmuS: The Amused Speech Database (2017) — 25 citations
- An empirical study on the effectiveness of images in Multimodal Neural Machine Translation (2017) — 43 citations
- An HMM-based speech-smile synthesis system: An approach for amusement synthesis (2015) — 24 citations
- Breath and repeat: An attempt at enhancing speech-laugh synthesis quality (2015) — 9 citations
- Speech-laughs: An HMM-based approach for amused speech synthesis (2015) — 28 citations
- An HMM approach for synthesizing amused speech with a controllable intensity of smile (2015) — 18 citations
- Automatic speech recognition and speech variability: A review (2007) — 590 citations