Aleksandar Botev

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 134 researchers and is trusted by 101 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #2,832 of 35,345 by trust here, #5,252 by citations
• Optimization & Training — 38 citations into it; #952 of 2,601 by trust here, #1,509 by citations
• Computer Vision and Pattern Recognition — 37 citations into it; #3,506 of 21,575 by trust here, #4,690 by citations
• Probabilistic & Bayesian Methods — 30 citations into it; #641 of 1,525 by trust here, #865 by citations
• Statistical and Nonlinear Physics — 15 citations into it; #2,234 of 10,127 by trust here, #3,382 by citations
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #457 of 3,145 by trust here, #866 by citations
• Generative Models — 12 citations into it

Whom Aleksandar Botev trusts overall

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

1. James Martens
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Diederik P. Kingma
8. Radford M. Neal
9. Andriy Mnih
10. Shun-ichi Amari

Who trusts Aleksandar Botev overall

1. Hippolyt Ritter
2. James Martens
3. Tyler L. Hayes
4. Janghyeon Lee
5. Jorge Armando Mendez Mendez
6. Frederik Kunstner
7. Matthias Bauer
8. Wesley J. Maddox
9. Philipp Hennig
10. Timothée Lesort

Papers from Aleksandar Botev's perspective

Ordered by how strongly the researchers Aleksandar Botev 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. 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

• Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting (2018) — 384 citations
• Practical Gauss-Newton Optimisation for Deep Learning (2017) — 266 citations
• Hamiltonian Generative Networks (2019) — 255 citations
• A Scalable Laplace Approximation for Neural Networks (2018) — 513 citations
• Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification (2017) — 28 citations