Sami Sarsa
Computer Science Applications · 2022–2023
A researcher in the Science corpus: 6 papers; trusts 12 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Science Applications — 71 citations into it; #708 of 1,855 by trust here, #465 by citations
- Information Systems — 61 citations into it; #5,292 of 8,771 by trust here, #2,539 by citations
- Human-Computer Interaction — 43 citations into it
- Software — 28 citations into it; #805 of 1,227 by trust here, #439 by citations
- Code Generation & Program Synthesis — 26 citations into it; #133 of 220 by trust here, #98 by citations
- Large Language Models — 23 citations into it; #410 of 799 by trust here, #576 by citations
- Developmental and Educational Psychology — 18 citations into it
- Cognitive Science & Psychology — 13 citations into it
Whom Sami Sarsa trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Paul Denny
- Raymond Lister
- Nea Pirttinen
- Juho Leinonen
- Kirsti Ala-Mutka
- Patrick Schramowski
- Mark Chen
- Andrew Luxton-Reilly
- Tom B. Brown
- Elliot Soloway
Who trusts Sami Sarsa overall
- Paul Denny
- Juho Leinonen
- Stephen MacNeil
- Majeed Kazemitabaar
- Arto Hellas
- Viraj Kumar
- James Prather
- Andrew Tran
- Brett A. Becker
Papers from Sami Sarsa's perspective
Ordered by how strongly the researchers Sami Sarsa trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Hierarchical Text-Conditional Image Generation with CLIP Latents (arXiv.org)
- Language Models are Few-Shot Learners (Neural Information Processing Systems)
- Evaluating Large Language Models Trained on Code (arXiv.org)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet classification with deep convolutional neural networks
- Extracting Training Data from Large Language Models
- Language Models are Unsupervised Multitask Learners
- On the Opportunities and Risks of Foundation Models
Their papers
- Comparing Code Explanations Created by Students and Large Language Models (2023) — 209 citations
- Computing Education in the Era of Generative AI (2023) — 325 citations
- Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models (2022) — 502 citations
- Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book (2022) — 251 citations
- Robosourcing Educational Resources - Leveraging Large Language Models for Learnersourcing (2022) — 40 citations
- Using Large Language Models to Enhance Programming Error Messages (2022) — 245 citations