Vassil Chatalbashev

Computer Vision and Pattern Recognition · 2004–2005

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 103 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 12 citations into it; #3,128 of 21,575 by trust here, #5,333 by citations
• Artificial Intelligence — 5 citations into it; #4,396 of 35,345 by trust here, #7,413 by citations
• Natural Language Processing — 5 citations into it; #1,456 of 5,688 by trust here, #2,156 by citations

Whom Vassil Chatalbashev trusts overall

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

1. B. Taskar
2. Michael Collins
3. Jon M. Kleinberg
4. Yuri Boykov
5. Daphne Koller
6. Vladimir Kolmogorov
7. Robert E. Schapire
8. Serge J. Belongie
9. Jonathan S. Yedidia
10. Ramin Zabih

Who trusts Vassil Chatalbashev overall

1. B. Taskar
2. Nathan D. Ratliff
3. Simon Lacoste-Julien
4. Hema Swetha Koppula
5. J. Bagnell
6. Abhishek Anand
7. J. Kolter
8. Brandon Amos
9. Tamir Hazan
10. Alexander Gerhard Schwing

Papers from Vassil Chatalbashev's perspective

Ordered by how strongly the researchers Vassil Chatalbashev 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
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 structured prediction models: a large margin approach (2005) — 575 citations
• Learning associative Markov networks (2004) — 204 citations
• Discriminative learning of Markov random fields for segmentation of 3D scan data (2005) — 433 citations