Jayavardhan Reddy Peddamail
Artificial Intelligence · 2019
A researcher in the Science corpus: 1 paper; trusts 73 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 30 citations into it
- Natural Language Processing — 23 citations into it
- Information Systems — 12 citations into it
- Computer Vision and Pattern Recognition — 10 citations into it
- Reinforcement Learning — 8 citations into it
- Evaluation & Benchmarks — 7 citations into it
- Model Architectures — 7 citations into it
- Code Generation & Program Synthesis — 5 citations into it
Whom Jayavardhan Reddy Peddamail trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Geoffrey E. Hinton
- Ronald J. Williams
- Diederik P. Kingma
- Yoshua Bengio
- Sepp Hochreiter
- J. Urgen Schmidhuber
- Alex Krizhevsky
- Richard S. Sutton
- Oriol Vinyals
Who trusts Jayavardhan Reddy Peddamail overall
Papers from Jayavardhan Reddy Peddamail's perspective
Ordered by how strongly the researchers Jayavardhan Reddy Peddamail 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
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning (2019) — 109 citations