Gideon Dror
Artificial Intelligence · 2004–2013
A researcher in the Science corpus: 15 papers; trusts 62 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 62 citations into it; #8,847 of 35,345 by trust here, #7,858 by citations
- Computer Vision and Pattern Recognition — 32 citations into it; #9,838 of 21,575 by trust here, #9,722 by citations
- Evaluation & Benchmarks — 22 citations into it; #1,161 of 3,147 by trust here, #1,517 by citations
- Statistical Learning Theory — 21 citations into it; #734 of 1,038 by trust here, #715 by citations
- Hardware and Architecture — 20 citations into it; #1,542 of 1,551 by trust here, #1,153 by citations
- Representation & Self-Supervised Learning — 20 citations into it; #2,695 of 3,145 by trust here, #2,120 by citations
- Information Systems — 19 citations into it; #3,131 of 8,771 by trust here, #5,942 by citations
- Optimization & Training — 16 citations into it
Whom Gideon Dror trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yehuda Koren
- Jerome H. Friedman
- Yoshua Bengio
- Trevor J. Hastie
- Harald Steck
- Isabelle M Guyon
- Radford M. Neal
- Leo Breiman
- Geoffrey E. Hinton
- Robert Tibshirani
Who trusts Gideon Dror overall
- Noam Koenigstein
- Parikshit Ram
- Prem Gopalan
- Federico Monti
- Ulrich Paquet
- Chenhao Tan
- Yoram Bachrach
- Alexander G. Gray
- Yongfeng Zhang
- Jake M. Hofman
Papers from Gideon Dror's perspective
Ordered by how strongly the researchers Gideon Dror trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Hash Kernels for Structured Data (2009) — 240 citations
- Unsupervised and transfer learning challenge (2011) — 35 citations
- Large-scale learning of word relatedness with constraints (2012) — 275 citations
- Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge (2008) — 34 citations
- Model Selection: Beyond the Bayesian/Frequentist Divide (2010) — 183 citations
- PUF Modeling Attacks on Simulated and Silicon Data (2013) — 567 citations
- Churn prediction in new users of Yahoo! answers (2012) — 122 citations
- The Yahoo! Music Dataset and KDD-Cup '11 (2012) — 326 citations
- Results of the Active Learning Challenge (2011) — 102 citations
- Recommendations : Modeling Music Ratings with Temporal Dynamics and Item Taxonomy (2011)
- Modeling attacks on physical unclonable functions (2010) — 1,023 citations
- Hash Kernels (2009) — 89 citations
- Feature Selection via Coalitional Game Theory (2007) — 153 citations
- Agnostic Learning vs. Prior Knowledge Challenge (2007) — 38 citations
- Result Analysis of the NIPS 2003 Feature Selection Challenge (2004) — 724 citations