Botond Cseke

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 124 researchers and is trusted by 651 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #1,725 of 35,345 by trust here, #3,467 by citations
• Probabilistic & Bayesian Methods — 40 citations into it; #169 of 1,525 by trust here, #134 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #1,719 of 21,575 by trust here, #2,625 by citations
• Generative Models — 35 citations into it; #137 of 1,956 by trust here, #267 by citations
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Statistics and Probability — 9 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Botond Cseke trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Tom Minka
4. David John Cameron MacKay
5. Alex Graves
6. Yoshua Bengio
7. Max Welling
8. Radford M. Neal
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Botond Cseke overall

1. Balaji Lakshminarayanan
2. Jiaming Song
3. Stefano Ermon
4. Yang Song
5. Michael Arbel
6. Ben Poole
7. Lars M. Mescheder
8. Garrett Wilson
9. Sergey Levine
10. Aditya Grover

Papers from Botond Cseke's perspective

Ordered by how strongly the researchers Botond Cseke 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization (2016) — 1,831 citations
• Learning Hierarchical Priors in VAEs (2019) — 110 citations
• Network of Epistatic Interactions Within a Yeast snoRNA (2016) — 116 citations
• Continual Learning with Bayesian Neural Networks for Non-Stationary Data (2020) — 82 citations
• Supplementary Materials for : “ f-GAN : Training Generative Neural Samplers using Variational Divergence Minimization ” (2016)