Jan Profant

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #22,648 of 35,345 by trust here, #25,809 by citations
• Speech & Audio — 15 citations into it; #710 of 1,076 by trust here, #900 by citations
• Signal Processing — 14 citations into it; #4,674 of 7,572 by trust here, #5,086 by citations

Whom Jan Profant trusts overall

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

1. Radford M. Neal
2. Patrick Kenny
3. Matthew J. Beal
4. Joon Son Chung
5. Arsha Nagrani
6. Andrew Zisserman
7. Jiankang Deng
8. Kaiming He
9. David Snyder

Who trusts Jan Profant overall

1. Hervé Bredin
2. Shota Horiguchi
3. Shinji Watanabe

Papers from Jan Profant's perspective

Ordered by how strongly the researchers Jan Profant 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Long Short-Term Memory
10. Going deeper with convolutions

Their papers

• Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks (2020) — 261 citations