Arno Solin

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 5 papers; trusts 319 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #15,257 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #19,779 of 21,575 by trust here, #18,266 by citations
• Generative Models — 59 citations into it; #1,798 of 1,956 by trust here, #1,766 by citations
• Probabilistic & Bayesian Methods — 25 citations into it
• Statistical and Nonlinear Physics — 21 citations into it; #3,636 of 10,127 by trust here, #3,979 by citations
• Finance — 15 citations into it
• Optimization & Training — 13 citations into it
• Statistics and Probability — 12 citations into it

Whom Arno Solin trusts overall

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

1. Diederik P. Kingma
2. Radford M. Neal
3. Geoffrey E. Hinton
4. Kaiming He
5. Alex Krizhevsky
6. Yoshua Bengio
7. Haikady N. Nagaraja
8. Simo Särkkä
9. Yann LeCun
10. Tero Karras

Who trusts Arno Solin overall

1. Stefano Peluchetti
2. Arash Vahdat
3. Tim Dockhorn
4. Karsten Kreis
5. Ari Heljakka
6. Joan Serrà
7. Vinesh Rajpaul
8. Feng Yin

Papers from Arno Solin's perspective

Ordered by how strongly the researchers Arno Solin 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Pioneer Networks: Progressively Growing Generative Autoencoder (2018) — 41 citations
• Applied Stochastic Differential Equations (2019) — 553 citations
• Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders (2019) — 15 citations
• State Space Gaussian Processes with Non-Gaussian Likelihood (2018) — 35 citations
• Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering (2013) — 279 citations