Brecht Desplanques

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 44 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #17,178 of 35,345 by trust here, #23,731 by citations
• Signal Processing — 23 citations into it; #3,459 of 7,572 by trust here, #4,560 by citations
• Speech & Audio — 16 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Model Architectures — 8 citations into it
• Optimization & Training — 8 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Brecht Desplanques trusts overall

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

1. David Snyder
2. Diederik P. Kingma
3. Sergey Ioffe
4. Kaiming He
5. Leslie N. Smith
6. Joon Son Chung
7. Arsha Nagrani
8. Andrew Zisserman
9. Daniel Povey
10. Geoffrey E. Hinton

Who trusts Brecht Desplanques overall

1. Hervé Bredin
2. Hyeong-Seok Choi
3. Alexis Plaquet
4. Jenthe Thienpondt
5. Juheon Lee
6. Mirco Ravanelli
7. Miao Zhao
8. Jinhyeok Yang

Papers from Brecht Desplanques's perspective

Ordered by how strongly the researchers Brecht Desplanques 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification (2020) — 2,029 citations
• The Idlab Voxsrc-20 Submission: Large Margin Fine-Tuning and Quality-Aware Score Calibration in DNN Based Speaker Verification (2020) — 115 citations