C. Jermaine
Information Systems · 2008–2017
A researcher in the Science corpus: 6 papers; trusts 77 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 75 citations into it; #1,851 of 8,771 by trust here, #4,341 by citations
- Artificial Intelligence — 63 citations into it
- Natural Language Processing — 62 citations into it
- Software — 54 citations into it; #253 of 1,227 by trust here, #534 by citations
- Code Generation & Program Synthesis — 35 citations into it; #83 of 220 by trust here, #170 by citations
- Signal Processing — 25 citations into it; #5,537 of 7,572 by trust here, #5,086 by citations
- Program Analysis & Verification — 17 citations into it
- Computer Vision and Pattern Recognition — 15 citations into it
Whom C. Jermaine trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Veselin Raychev
- Diederik P. Kingma
- Martin T. Vechev
- Geoffrey E. Hinton
- Tomas Mikolov
- Radford M. Neal
- Anh Tuan Nguyen
- Yoshua Bengio
- Huaiyu Zhu
- Andrew Y. Ng
Who trusts C. Jermaine overall
Papers from C. Jermaine's perspective
Ordered by how strongly the researchers C. Jermaine 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Bayesian specification learning for finding API usage errors (2017) — 47 citations
- Finding Likely Errors with Bayesian Specifications (2017) — 5 citations
- Data-Driven Program Completion (2017) — 12 citations
- Neural Sketch Learning for Conditional Program Generation (2017) — 141 citations
- Bayesian Sketch Learning for Program Synthesis (2017) — 38 citations
- MCDB: a monte carlo approach to managing uncertain data (2008) — 312 citations