Michele Tufano
Information Systems · 2016–2023
A researcher in the Science corpus: 12 papers; trusts 173 researchers and is trusted by 44 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 227 citations into it; #616 of 8,771 by trust here, #126 by citations
- Artificial Intelligence — 209 citations into it
- Software — 161 citations into it; #100 of 1,227 by trust here, #4 by citations
- Natural Language Processing — 151 citations into it; #3,225 of 5,688 by trust here, #1,004 by citations
- Code Generation & Program Synthesis — 71 citations into it; #38 of 220 by trust here, #37 by citations
- Signal Processing — 61 citations into it; #1,620 of 7,572 by trust here, #557 by citations
- Computer Vision and Pattern Recognition — 57 citations into it
- Program Analysis & Verification — 49 citations into it; #252 of 301 by trust here, #72 by citations
Whom Michele Tufano trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Ilya Sutskever
- Alex Graves
- Geoffrey E. Hinton
- Sepp Hochreiter
- Kyunghyun Cho
- Dzmitry Bahdanau
- Oriol Vinyals
- J. Urgen Schmidhuber
- Diederik P. Kingma
Who trusts Michele Tufano overall
- Baptiste Rozière
- Yi Li
- Saikat Chakraborty
- Hlib Babii
- Dawn Drain
- Shaked Brody
- Zimin Chen
- Michael Pradel
- Federico Cassano
- Alexey Svyatkovskiy
Papers from Michele Tufano's perspective
Ordered by how strongly the researchers Michele Tufano 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
- 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
- GraphCodeBERT: Pre-training Code Representations with Data Flow (2020) — 1,778 citations
- InferFix: End-to-End Program Repair with LLMs (2023) — 324 citations
- Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation (2023) — 51 citations
- METHODS2TEST: A dataset of focal methods mapped to test cases (2022) — 45 citations
- CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021) — 1,545 citations
- Unit Test Case Generation with Transformers (2020) — 292 citations
- On Learning Meaningful Assert Statements for Unit Test Cases (2020) — 196 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
- SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair (2018) — 554 citations
- When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017) — 473 citations
- Deep learning code fragments for code clone detection (2016) — 690 citations