Daniel Kroening

Artificial Intelligence · 2003–2018

A researcher in the Science corpus: 7 papers; trusts 43 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 146 citations into it; #15,987 of 35,345 by trust here, #15,866 by citations
• Safety, Robustness & Interpretability — 116 citations into it
• Computer Vision and Pattern Recognition — 65 citations into it
• Software — 38 citations into it; #334 of 1,227 by trust here, #298 by citations
• Computational Theory and Mathematics — 32 citations into it; #2,042 of 12,182 by trust here, #3,117 by citations
• Program Analysis & Verification — 18 citations into it; #107 of 301 by trust here, #220 by citations
• Hardware and Architecture — 14 citations into it; #387 of 1,551 by trust here, #642 by citations
• Computer Networks and Communications — 7 citations into it

Whom Daniel Kroening trusts overall

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

1. Edmund M. Clarke
2. Ian J. Goodfellow
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Yoshua Bengio
7. Alex Krizhevsky
8. Thomas A. Henzinger
9. Hubert Garavel
10. Christian Szegedy

Who trusts Daniel Kroening overall

1. Armando Solar-Lezama
2. Dirk Beyer
3. Frank Ghenassia
4. Vijay Victor D'Silva
5. Rastislav Bodík
6. Edmund M. Clarke
7. Andreas Holzer
8. Kenneth L. McMillan
9. Jonathan M. McCune
10. M. Erkan Keremoglu

Papers from Daniel Kroening's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability (2018) — 607 citations
• A Tool for Checking ANSI-C Programs (2004) — 1,554 citations
• SATABS: SAT-Based Predicate Abstraction for ANSI-C (2005) — 307 citations
• Behavioral consistency of C and Verilog programs using bounded model checking (2003) — 314 citations
• Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for L0 Norm (2018) — 38 citations
• Concolic Testing for Deep Neural Networks (2018) — 351 citations
• A Survey of Automated Techniques for Formal Software Verification (2008) — 441 citations