Jacob A. Harer
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 30 citations into it; #25,922 of 35,345 by trust here, #33,294 by citations
- Computer Vision and Pattern Recognition — 25 citations into it
- Natural Language Processing — 25 citations into it
- Information Systems — 15 citations into it; #5,296 of 8,771 by trust here, #3,876 by citations
- Software — 12 citations into it
- Model Architectures — 5 citations into it; #1,688 of 2,278 by trust here, #2,215 by citations
Whom Jacob A. Harer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Richard Socher
- Ilya Sutskever
- Sepp Hochreiter
- Jacob Devlin
- Alex Krizhevsky
- Dzmitry Bahdanau
- Yann LeCun
Who trusts Jacob A. Harer overall
- Wasi Uddin Ahmad
- Seohyun Kim
- Saikat Chakraborty
- Rebecca L. Russell
- Jinman Zhao
- Baishakhi Ray
- Christopher P. Reale
Papers from Jacob A. Harer's perspective
Ordered by how strongly the researchers Jacob A. Harer 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data (2019) — 48 citations
- 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