Massimiliano Di Penta
Information Systems · 2005–2018
A researcher in the Science corpus: 10 papers; trusts 42 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 112 citations into it; #2,775 of 8,771 by trust here, #1,142 by citations
- Software — 71 citations into it; #1,141 of 1,227 by trust here, #243 by citations
- Artificial Intelligence — 53 citations into it; #17,429 of 35,345 by trust here, #16,771 by citations
- Signal Processing — 22 citations into it
- Computer Networks and Communications — 19 citations into it; #5,047 of 9,886 by trust here, #5,733 by citations
- Natural Language Processing — 19 citations into it; #5,178 of 5,688 by trust here, #3,421 by citations
- Code Generation & Program Synthesis — 10 citations into it
- Model Architectures — 7 citations into it
Whom Massimiliano Di Penta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Carlos Artemio Coello Coello
- Radu Marinescu
- Jan Mendling
- Premkumar T. Devanbu
- Michele Lanza
- Matjaz B. Juric
- Naouel Moha
- David E. Goldberg
- Andrian Marcus
- Shyam R. Chidamber
Who trusts Massimiliano Di Penta overall
- Mark Harman
- Andrea Arcuri
- Lionel Claude Briand
- Manfred Reichert
- Fabio Palomba
- Gerardo Canfora
- Michele Tufano
- Miltiadis Allamanis
- Tam The Nguyen
Papers from Massimiliano Di Penta's perspective
Ordered by how strongly the researchers Massimiliano Di Penta trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- 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)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- ImageNet: A large-scale hierarchical image database
Their papers
- How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms (2013) — 337 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
- A Hidden Markov Model to detect coded information islands in free text (2013) — 21 citations
- An Exploratory Study of the Impact of Code Smells on Software Change-proneness (2009) — 339 citations
- Is it a bug or an enhancement?: a text-based approach to classify change requests (2008) — 425 citations
- A framework for QoS-aware binding and re-binding of composite web services (2008) — 333 citations
- An empirical study on the evolution of design patterns (2007) — 413 citations
- An approach for QoS-aware service composition based on genetic algorithms (2005) — 991 citations