Gabriele Bavota
Information Systems · 2017–2022
A researcher in the Science corpus: 9 papers; trusts 105 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 145 citations into it; #6,295 of 8,771 by trust here, #1,463 by citations
- Software — 100 citations into it; #1,073 of 1,227 by trust here, #215 by citations
- Artificial Intelligence — 99 citations into it
- Natural Language Processing — 69 citations into it; #5,181 of 5,688 by trust here, #3,107 by citations
- Signal Processing — 35 citations into it
- Code Generation & Program Synthesis — 28 citations into it
- Computer Vision and Pattern Recognition — 25 citations into it
- Model Architectures — 23 citations into it
Whom Gabriele Bavota trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Alex Graves
- Radu Marinescu
- Yoshua Bengio
- Miltiadis Allamanis
- Geoffrey E. Hinton
- Sepp Hochreiter
- Kyunghyun Cho
- Dzmitry Bahdanau
- Premkumar T. Devanbu
Who trusts Gabriele Bavota overall
Papers from Gabriele Bavota's perspective
Ordered by how strongly the researchers Gabriele Bavota trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks (2021) — 285 citations
- AI-Driven Development Is Here: Should You Worry? (2022) — 66 citations
- To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? (2022) — 32 citations
- On Learning Meaningful Assert Statements for Unit Test Cases (2020) — 196 citations
- Taxonomy of Real Faults in Deep Learning Systems (2019) — 363 citations
- On Learning Meaningful Code Changes Via Neural Machine Translation (2019) — 249 citations
- An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018) — 450 citations
- When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017) — 473 citations
- On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation (2017) — 338 citations