Nicolas Chapados

Artificial Intelligence · 2014–2024

A researcher in the Science corpus: 6 papers; trusts 62 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #8,554 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Natural Language Processing — 26 citations into it
• Time-Series & Forecasting — 26 citations into it; #39 of 381 by trust here, #60 by citations
• Management Science and Operations Research — 24 citations into it; #4,885 of 11,946 by trust here, #2,402 by citations
• Statistics and Probability — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Model Architectures — 13 citations into it

Whom Nicolas Chapados trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Joseph Hilbe
2. Radford M. Neal
3. Tom Minka
4. Steven L. Scott
5. Timothy A. Davis
6. Tom Heskes Onno Zoeter
7. L. Shenstone
8. Håvard Rue
9. Marc Peter Deisenroth
10. Ralph D. Snyder

Who trusts Nicolas Chapados overall

1. Valentin Flunkert
2. Matthias W. Seeger
3. David Salinas
4. Syama Sundar Rangapuram
5. Jan Gasthaus
6. SHIYANG LI
7. Bernie Wang
8. Haixu Wu
9. Konstantinos Benidis
10. Xiaoyong Jin

Papers from Nicolas Chapados's perspective

Ordered by how strongly the researchers Nicolas Chapados trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019) — 1,690 citations
• LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders (2024) — 526 citations
• StarCoder 2 and The Stack v2: The Next Generation (2024) — 698 citations
• Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model (2017) — 557 citations
• HeMIS: Hetero-Modal Image Segmentation (2016) — 355 citations
• Effective Bayesian Modeling of Groups of Related Count Time Series (2014) — 33 citations