György Kovács

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 5 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #19,183 of 35,345 by trust here, #31,361 by citations
• Signal Processing — 26 citations into it
• Electrical and Electronic Engineering — 7 citations into it; #6,268 of 16,539 by trust here, #16,387 by citations

Whom György Kovács trusts overall

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

1. Nitesh V. Chawla
2. Geoffrey E. Hinton
3. László Tóth
4. Chumphol Bunkhumpornpat
5. Alberto Fernández
6. Georgios Douzas
7. Yajie Miao

Who trusts György Kovács overall

1. Tarid Wongvorachan
2. Surina He
3. Daniel S. Park
4. Okan Bulut

Papers from György Kovács's perspective

Ordered by how strongly the researchers György Kovács 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. A Fast Learning Algorithm for Deep Belief Nets
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• 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
• An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets (2019) — 299 citations