Pierre Colombo
Artificial Intelligence · 2021–2023
A researcher in the Science corpus: 5 papers; trusts 107 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 338 citations into it
- Natural Language Processing — 270 citations into it
- Evaluation & Benchmarks — 142 citations into it
- Computer Vision and Pattern Recognition — 85 citations into it
- Large Language Models — 85 citations into it
- Representation & Self-Supervised Learning — 24 citations into it
- General Social Sciences — 12 citations into it
Whom Pierre Colombo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ondrej Bojar
- Chin-Yew Lin
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Maxime Peyrard
- Alex Krizhevsky
- Richard Socher
- Satanjeev Banerjee
Who trusts Pierre Colombo overall
Papers from Pierre Colombo's perspective
Ordered by how strongly the researchers Pierre Colombo trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- 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)
- Gradient-based learning applied to document recognition
- 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)
- ImageNet: A large-scale hierarchical image database
Their papers
- The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023) — 217 citations
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021) — 104 citations
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
- Automatic Text Evaluation through the Lens of Wasserstein Barycenters (2021) — 44 citations
- InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation (2021) — 52 citations