Milod Kazerounian
Information Systems · 2020–2021
A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 20 citations into it; #6,727 of 8,771 by trust here, #8,494 by citations
- Artificial Intelligence — 18 citations into it; #22,876 of 35,345 by trust here, #30,347 by citations
- Program Analysis & Verification — 17 citations into it; #146 of 301 by trust here, #248 by citations
- Software — 9 citations into it
- Natural Language Processing — 8 citations into it
- Software Engineering — 8 citations into it; #102 of 113 by trust here, #95 by citations
- Code Generation & Program Synthesis — 7 citations into it
- Signal Processing — 5 citations into it
Whom Milod Kazerounian trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michael Pradel
- Gregory R. Koch
- Diederik P. Kingma
- Miltiadis Allamanis
- Vincent J. Hellendoorn
- Nils Reimers
- Michael Furr
- Earl T. Barr
- Jimmy Ba
- Georgios Gousios
Who trusts Milod Kazerounian overall
Papers from Milod Kazerounian's perspective
Ordered by how strongly the researchers Milod Kazerounian trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
Their papers
- SimTyper: sound type inference for Ruby using type equality prediction (2021) — 10 citations
- Sound, heuristic type annotation inference for Ruby (2020) — 16 citations