Tomo Lazovich

Computer Vision and Pattern Recognition · 2018

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

Subfields

• Computer Vision and Pattern Recognition — 19 citations into it

Whom Tomo Lazovich trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Sepp Hochreiter
4. Diederik P. Kingma
5. Ian J. Goodfellow
6. Ilya Sutskever
7. Alex Krizhevsky
8. Martín Arjovsky
9. J. Urgen Schmidhuber
10. Yann LeCun

Papers from Tomo Lazovich's perspective

Ordered by how strongly the researchers Tomo Lazovich trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Automated Vulnerability Detection in Source Code Using Deep Representation Learning (2018) — 695 citations
• Learning to Repair Software Vulnerabilities with Generative Adversarial Networks (2018) — 83 citations