Lintao Zhang

Artificial Intelligence · 2001–2020

A researcher in the Science corpus: 9 papers; trusts 47 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #12,115 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 54 citations into it
• Computer Networks and Communications — 21 citations into it; #1,558 of 9,886 by trust here, #789 by citations
• Signal Processing — 19 citations into it; #7,361 of 7,572 by trust here, #2,664 by citations
• Model Architectures — 15 citations into it
• Hardware and Architecture — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it; #864 of 12,182 by trust here, #704 by citations
• Electrical and Electronic Engineering — 7 citations into it

Whom Lintao Zhang trusts overall

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

1. E. Goldberg
2. Daniel Jackson
3. Joao Marques-Silva
4. Yakov Novikov
5. Matthew W. Moskewicz
6. Karem A. Sakallah
7. Alex Krizhevsky

Who trusts Lintao Zhang overall

1. Martin Gebser
2. Armin Biere
3. Gilles Audemard
4. Clark W. Barrett
5. Kenneth L. McMillan
6. Zhaohui Fu
7. Paul Beame
8. Benjamin Kaufmann
9. Sharad Malik
10. Oleksandr Polozov

Papers from Lintao Zhang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. ImageNet Large Scale Visual Recognition Challenge
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Going deeper with convolutions
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization (2019) — 80 citations
• Chaff: engineering an efficient SAT solver (2001) — 3,841 citations
• Efficient conflict driven learning in a Boolean satisfiability solver (2001) — 898 citations
• Validating SAT solvers using an independent resolution-based checker: practical implementations and other applications (2003) — 339 citations
• The Quest for Efficient Boolean Satisfiability Solvers (2002) — 357 citations
• Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks (2020) — 200 citations
• Occlumency: Privacy-preserving Remote Deep-learning Inference Using SGX (2019) — 150 citations
• Vigilante: end-to-end containment of internet worms (2005) — 539 citations
• Cha : Engineering an e cient SAT solver (2001) — 746 citations