Ulrich Paquet

Artificial Intelligence · 2011–2021

A researcher in the Science corpus: 8 papers; trusts 256 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #5,294 of 35,345 by trust here, #6,517 by citations
• Computer Vision and Pattern Recognition — 71 citations into it; #6,676 of 21,575 by trust here, #6,795 by citations
• Probabilistic & Bayesian Methods — 34 citations into it; #865 of 1,525 by trust here, #485 by citations
• Safety, Robustness & Interpretability — 25 citations into it
• Model Architectures — 23 citations into it; #998 of 2,278 by trust here, #894 by citations
• Generative Models — 20 citations into it; #1,002 of 1,956 by trust here, #1,200 by citations
• Natural Language Processing — 20 citations into it
• Information Systems — 19 citations into it; #965 of 8,771 by trust here, #2,912 by citations

Whom Ulrich Paquet trusts overall

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

1. Radford M. Neal
2. Ruslan Salakhutdinov
3. Geoffrey E. Hinton
4. Yehuda Koren
5. Diederik P. Kingma
6. Yoshua Bengio
7. Kaiming He
8. Michael I. Jordan
9. Alex Krizhevsky
10. Sepp Hochreiter

Who trusts Ulrich Paquet overall

1. Oren Barkan
2. José Miguel Hernández-Lobato
3. Prem Gopalan
4. Noam Koenigstein
5. Neil Houlsby
6. Shay Ben-Elazar
7. Jake M. Hofman
8. Shakir Mohamed
9. Weike Pan
10. David M. Blei

Papers from Ulrich Paquet's perspective

Ordered by how strongly the researchers Ulrich Paquet 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sequential Neural Models with Stochastic Layers (2016) — 441 citations
• A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning (2017) — 320 citations
• Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces (2014) — 166 citations
• Recurrent Relational Networks (2017) — 168 citations
• One-class collaborative filtering with random graphs (2013) — 111 citations
• A hierarchical model for ordinal matrix factorization (2011) — 42 citations
• Acquisition of chess knowledge in AlphaZero (2021) — 204 citations
• Recurrent Relational Networks for Complex Relational Reasoning (2018) — 31 citations