László Tóth

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 4 papers; trusts 76 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #6,676 of 35,345 by trust here, #22,463 by citations
• Signal Processing — 60 citations into it; #1,025 of 7,572 by trust here, #4,201 by citations
• Speech & Audio — 24 citations into it; #365 of 1,076 by trust here, #900 by citations
• Model Architectures — 23 citations into it; #737 of 2,278 by trust here, #2,037 by citations
• Natural Language Processing — 18 citations into it
• Optimization & Training — 12 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it

Whom László Tóth trusts overall

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

1. Geoffrey E. Hinton
2. Abdel-rahman Mohamed
3. George E. Dahl
4. Hervé Bourlard
5. Hamed Ketabdar
6. Tara N. Sainath
7. Yoshua Bengio
8. Ossama Abdel-Hamid
9. Li Deng
10. Xavier Glorot

Who trusts László Tóth overall

1. Leonardo Badino
2. Andy T. Liu
3. Chigozie Nwankpa
4. Chiyuan Zhang
5. Neil Zeghidour
6. Tomasz Szandala
7. Daniel S. Park
8. Shang-Wen Li
9. Winifred L. Ijomah
10. Stephen Voinea

Papers from László Tóth's perspective

Ordered by how strongly the researchers László Tóth trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Phone recognition with hierarchical convolutional deep maxout networks (2015) — 94 citations
• A Perceptually Inspired Data Augmentation Method for Noise Robust CNN Acoustic Models (2018) — 20 citations
• Increasing the robustness of CNN acoustic models using autoregressive moving average spectrogram features and channel dropout (2017) — 41 citations
• Convolutional deep maxout networks for phone recognition (2014) — 44 citations