Alexej Klushyn

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 45 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it
• Generative Models — 17 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Optimization & Training — 8 citations into it
• Meta & Continual Learning — 5 citations into it

Whom Alexej Klushyn trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Danilo Jimenez Rezende
4. Radford M. Neal
5. Yoshua Bengio
6. Yann LeCun
7. Ruslan Salakhutdinov
8. Hugo Larochelle
9. Alex Graves
10. Max Welling

Who trusts Alexej Klushyn overall

1. Fei Ye
2. Louay Hazami
3. Jyoti Aneja
4. Adrian G. Bors
5. Arash Vahdat
6. Rayhane Mama
7. Jan Kautz
8. Alexander Gerhard Schwing

Papers from Alexej Klushyn's perspective

Ordered by how strongly the researchers Alexej Klushyn 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. 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. 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

• Learning Hierarchical Priors in VAEs (2019) — 110 citations
• Continual Learning with Bayesian Neural Networks for Non-Stationary Data (2020) — 82 citations