Daniel Hsu

Artificial Intelligence · 2017

A researcher in the Science corpus: 3 papers; trusts 87 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #14,160 of 35,345 by trust here, #30,347 by citations
• Signal Processing — 10 citations into it; #2,742 of 7,572 by trust here, #6,355 by citations
• Model Architectures — 8 citations into it
• Building and Construction — 6 citations into it; #1,368 of 2,909 by trust here, #2,508 by citations
• Control and Systems Engineering — 6 citations into it
• Management Science and Operations Research — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Daniel Hsu trusts overall

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

1. Yoshua Bengio
2. Alex Graves
3. Diederik P. Kingma
4. Sepp Hochreiter
5. J. Urgen Schmidhuber
6. Felix Alexander Gers
7. Soumya Ghosh
8. Geoffrey E. Hinton
9. David M. Blei
10. Takashi Kuremoto

Who trusts Daniel Hsu overall

1. David Charte
2. Benjamin Lindemann
3. Anastasia Borovykh
4. Konstantinos Benidis
5. Francisco Charte
6. Timo Müller
7. Sander M. Bohte
8. Syama Sundar Rangapuram
9. Salvador Garcia
10. Hannes Vietz

Papers from Daniel Hsu's perspective

Ordered by how strongly the researchers Daniel Hsu 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference (2017) — 21 citations
• Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder (2017) — 13 citations
• Time Series Forecasting Based on Augmented Long Short-Term Memory (2017) — 45 citations