Sebastian W. Ober

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 32 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Optimization & Training — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Sebastian W. Ober trusts overall

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

1. Diederik P. Kingma
2. Radford M. Neal
3. Alex Graves
4. Jasper Snoek
5. Geoffrey E. Hinton
6. Jaka Demsar
7. José Miguel Hernández-Lobato
8. Jimmy Ba
9. Yarin Gal
10. Yoshua Bengio

Papers from Sebastian W. Ober's perspective

Ordered by how strongly the researchers Sebastian W. Ober 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Understanding Variational Inference in Function-Space (2020) — 51 citations
• Benchmarking the Neural Linear Model for Regression (2019) — 49 citations