Colin B. Clement
Artificial Intelligence · 2020–2022
A researcher in the Science corpus: 5 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 128 citations into it
- Information Systems — 122 citations into it
- Natural Language Processing — 102 citations into it
- Software — 71 citations into it
- Code Generation & Program Synthesis — 55 citations into it
- Large Language Models — 32 citations into it
- Computer Vision and Pattern Recognition — 21 citations into it
- Prompting & Reasoning — 9 citations into it
Whom Colin B. Clement trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alexey Svyatkovskiy
- Miltiadis Allamanis
- Veselin Raychev
- Antonio Valerio Miceli Barone
- Geoffrey E. Hinton
- Ashish Vaswani
- Yoshua Bengio
- Michael Lewis
- Diederik P. Kingma
- Alec Radford
Who trusts Colin B. Clement overall
- Federico Cassano
- Wasi Uddin Ahmad
- Junjie Huang
- Hung Le
- Yue Wang
- Shubham Chandel
- Erik Nijkamp
- Naman Jain
- Jacob Austin
- Chenglong Wang
Papers from Colin B. Clement's perspective
Ordered by how strongly the researchers Colin B. Clement trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
Their papers
- PyMT5: Multi-mode Translation of Natural Language and Python Code with Transformers (2020) — 168 citations
- GraphCodeBERT: Pre-training Code Representations with Data Flow (2020) — 1,778 citations
- Training and Evaluating a Jupyter Notebook Data Science Assistant (2022) — 56 citations
- Execution-based Evaluation for Data Science Code Generation Models (2022) — 43 citations
- CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021) — 1,545 citations