Balázs Hidasi

Information Systems · 2012–2017

A researcher in the Science corpus: 8 papers; trusts 78 researchers and is trusted by 126 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 66 citations into it; #109 of 8,771 by trust here, #360 by citations
• Artificial Intelligence — 55 citations into it; #2,137 of 35,345 by trust here, #4,080 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #6,785 of 21,575 by trust here, #8,409 by citations
• Model Architectures — 40 citations into it; #289 of 2,278 by trust here, #201 by citations
• Recommender Systems — 36 citations into it; #31 of 233 by trust here, #48 by citations
• Management Science and Operations Research — 25 citations into it; #541 of 11,946 by trust here, #903 by citations
• Natural Language Processing — 16 citations into it
• Signal Processing — 15 citations into it; #4,612 of 7,572 by trust here, #5,829 by citations

Whom Balázs Hidasi trusts overall

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

1. Ruslan Salakhutdinov
2. Geoffrey E. Hinton
3. Yehuda Koren
4. Yoshua Bengio
5. Steffen Rendle
6. Sepp Hochreiter
7. Kyunghyun Cho
8. Alex Krizhevsky
9. Kaiming He
10. Ilya Sutskever

Who trusts Balázs Hidasi overall

1. Malte Ludewig
2. Shoujin Wang
3. Alexandros Karatzoglou
4. Fajie Yuan
5. Dietmar Jannach
6. Massimo Quadrana
7. Kun Zhou
8. Wang-Cheng Kang
9. Jiacheng Li
10. Julian McAuley

Papers from Balázs Hidasi's perspective

Ordered by how strongly the researchers Balázs Hidasi 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. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Recurrent neural networks (2013) — 997 citations
• Session-based Recommendations with Recurrent Neural Networks (2015) — 2,897 citations
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017) — 978 citations
• Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback (2012) — 152 citations
• Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks (2017) — 711 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations
• Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations (2016) — 544 citations
• General factorization framework for context-aware recommendations (2014) — 112 citations