Antanas Verikas

Artificial Intelligence · 2002–2011

A researcher in the Science corpus: 3 papers; trusts 51 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #19,101 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #7,572 of 21,575 by trust here, #10,677 by citations
• Statistics and Probability — 15 citations into it
• Accounting — 14 citations into it
• Management Science and Operations Research — 13 citations into it
• Statistical Learning Theory — 11 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom Antanas Verikas trusts overall

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

1. Neil D. Lawrence
2. Jaka Demsar
3. Ludmila I. Kuncheva
4. David John Cameron MacKay
5. Tin Kam Ho
6. Leo Breiman
7. Yoav Freund
8. Radford M. Neal
9. Geoffrey E. Hinton
10. Thomas G. Dietterich

Who trusts Antanas Verikas overall

1. Alain Rakotomamonjy
2. Robin Genuer
3. Handing Wang
4. Durga Prasad Muni
5. Marko Bohanec
6. Hua-zhen Wang
7. Ningshan Chen
8. Wouter G. Touw
9. Jean‐Michel Poggi
10. Yaochu Jin

Papers from Antanas Verikas's perspective

Ordered by how strongly the researchers Antanas Verikas 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

• Mining data with random forests: A survey and results of new tests (2011) — 721 citations
• Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010) — 167 citations
• Feature selection with neural networks (2002) — 356 citations