Maarten Versteegh
Artificial Intelligence · 2015–2017
A researcher in the Science corpus: 6 papers; trusts 67 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 74 citations into it; #5,314 of 35,345 by trust here, #5,533 by citations
- Speech & Audio — 42 citations into it; #269 of 1,076 by trust here, #248 by citations
- Signal Processing — 41 citations into it; #2,689 of 7,572 by trust here, #2,589 by citations
- Natural Language Processing — 24 citations into it
- Representation & Self-Supervised Learning — 24 citations into it
- Computer Vision and Pattern Recognition — 19 citations into it
- Model Architectures — 7 citations into it; #1,349 of 2,278 by trust here, #1,643 by citations
- Evaluation & Benchmarks — 6 citations into it; #1,226 of 3,147 by trust here, #1,591 by citations
Whom Maarten Versteegh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Aren Jansen
- Thomas Schatz
- Geoffrey E. Hinton
- Hongjie Chen
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Sharon Goldwater
- Ilya Sutskever
- Herman Kamper
Who trusts Maarten Versteegh overall
- Morgane Rivière
- Thomas Schatz
- Emmanuel Dupoux
- Ewan Dunbar
- Herman Kamper
- Evgeny Kharitonov
- Mathieu Bernard
- Cory Shain
- Sharon Goldwater
- Sakriani Watiasri Sakti
Papers from Maarten Versteegh's perspective
Ordered by how strongly the researchers Maarten Versteegh 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- The Zero Resource Speech Challenge 2015: Proposed Approaches and Results (2016) — 94 citations
- The zero resource speech challenge 2017 (2017) — 210 citations
- Learning Text Similarity with Siamese Recurrent Networks (2016) — 334 citations
- The zero resource speech challenge 2015 (2015) — 107 citations
- A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling (2015) — 75 citations
- A deep scattering spectrum — Deep Siamese network pipeline for unsupervised acoustic modeling (2016) — 39 citations