Institution · Mila - Quebec Artificial Intelligence Institute
Most cited papers from Mila - Quebec Artificial Intelligence Institute
What are the most cited papers from Mila - Quebec Artificial Intelligence Institute?
The most cited paper from Mila - Quebec Artificial Intelligence Institute is "Scientific discovery in the age of artificial intelligence" (Hanchen Wang et al., 2023), with 1,788 citations. Next come "Diffusion Models: A Comprehensive Survey of Methods and Applications" (1,565) and "Machine learning for combinatorial optimization: A methodological tour d'horizon" (1,453).
Showing 50 of 59 papers. Every paper with more than 158 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Scientific discovery in the age of artificial intelligence Hanchen Wang et al. · Nature | 2 Aug 2023 | 1,788 |
| 2 | Diffusion Models: A Comprehensive Survey of Methods and Applications Ling Qiang Yang, Zhilong Zhang, Yang Song et al. · ACM Computing Surveys | 30 Sep 2023 | 1,565 |
| 3 | Machine learning for combinatorial optimization: A methodological tour d'horizon Yoshua Bengio, Andrea Lodi, Antoine Prouvost · Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) | 2021 | 1,453 |
| 4 | Visualizing structure and transitions in high-dimensional biological data Kevin R. Moon, David van Dijk, Zheng Wang et al. · Nature Biotechnology | Dec 2019 | 1,203 |
| 5 | Toward Causal Representation Learning Bernhard Schölkopf, Francesco Locatello, Stefan M. Bauer et al. · Proceedings of the IEEE | 26 Feb 2021 | 1,149 |
| 6 | A deep learning framework for neuroscience Blake A. Richards et al. · Nature Neuroscience | 28 Oct 2019 | 1,123 |
| 7 | The default mode network in cognition: a topographical perspective Jonathan Smallwood, Boris C. Bernhardt, Robert Leech et al. · Nature reviews. Neuroscience | 5 Jul 2021 | 1,007 |
| 8 | Tackling Climate Change with Machine Learning Lynn H. Kaack et al. · OPUS 4 (Zuse Institute Berlin) | 7 Feb 2022 | 994 |
| 9 | Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI Baptiste Vasey et al. · Nature Medicine | May 2022 | 837 |
| 10 | AutoInt Weiping Song, Chence Shi, Zhiping Xiao et al. | 3 Nov 2019 | 773 |
| 11 | Artificial intelligence in COVID-19 drug repurposing Yadi Zhou, Fei Wang, Jian Guo Tang et al. · The Lancet Digital Health | 18 Sep 2020 | 662 |
| 12 | Machine learning for medical imaging: methodological failures and recommendations for the future Gaël Varoquaux, Veronika Cheplygina · npj Digital Medicine | 12 Apr 2022 | 652 |
| 13 | Attention Is All You Need In Speech Separation Cem Subakan, Mirco Ravanelli, Samuele Cornell et al. | 13 May 2021 | 645 |
| 14 | KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu et al. · Transactions of the Association for Computational Linguistics | Mar 2021 | 633 |
| 15 | Aligning artificial intelligence with climate change mitigation Lynn H. Kaack, Priya L. Donti, Emma Strubell et al. · Nature Climate Change | Jun 2022 | 531 |
| 16 | HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation Moein Heidari, Amirhossein Kazerouni, Milad Soltany et al. · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) | 2023 | 521 |
| 17 | Metrics reloaded: recommendations for image analysis validation Lena Maier‐Hein et al. · Nature Methods | Feb 2024 | 467 |
| 18 | Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative Charlene Ronquillo, Laura‐Maria Peltonen, Lisiane Pruinelli et al. · Journal of Advanced Nursing | 18 May 2021 | 434 |
| 19 | Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation Yusong Wu, Ke Chen, Tianyu Zhang et al. | 5 May 2023 | 427 |
| 20 | Tools and Techniques for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/COVID-19 Detection Seyed Hamid Safiabadi Tali, Jason J. LeBlanc, Zubi Sadiq et al. · Clinical Microbiology Reviews | 11 May 2021 | 418 |
| 21 | Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs Woojeong Jin, Meng Qu, Xisen Jin et al. | 2020 | 394 |
| 22 | Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture Mahmoud Assran, Quentin Duval, Ishan Misra et al. | Jun 2023 | 392 |
| 23 | Exploring single-cell data with deep multitasking neural networks Matthew Amodio, David van Dijk, Krishnan Srinivasan et al. · Nature Methods | 7 Oct 2019 | 352 |
| 24 | Mapping the landscape of Artificial Intelligence applications against COVID-19 Joseph Bullock, Alexandra Luccioni, Katherine Hoffman Pham et al. · Journal of Artificial Intelligence Research | 19 Nov 2020 | 347 |
| 25 | Autism spectrum heterogeneity: fact or artifact? Laurent Mottron, Danilo Bzdok · Molecular Psychiatry | 30 Apr 2020 | 343 |
| 26 | Graph Representation Learning William L. Hamilton · Synthesis lectures on artificial intelligence and machine learning | 15 Sep 2020 | 322 |
| 27 | Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark Xu-Yao Zhang, Yoshua Bengio, Cheng‐Lin Liu · Pattern Recognition | 9 Aug 2016 | 319 |
| 28 | Multimodal End-to-End Autonomous Driving Yi Xiao, Felipe Codevilla, Akhil Gurram et al. · IEEE Transactions on Intelligent Transportation Systems | 11 Aug 2020 | 319 |
| 29 | Utilizing graph machine learning within drug discovery and development Thomas Gaudelet, Ben Day, Arian R. Jamasb et al. · Briefings in Bioinformatics | 8 Apr 2021 | 314 |
| 30 | Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets Marc-André Schulz, B.T. Thomas Yeo, Joshua T Vogelstein et al. · Nature Communications | 25 Aug 2020 | 312 |
| 31 | Managing extreme AI risks amid rapid progress Yoshua Bengio et al. · Science | 20 May 2024 | 310 |
| 32 | Catalyzing next-generation Artificial Intelligence through NeuroAI Anthony M. Zador et al. · Nature Communications | 22 Mar 2023 | 303 |
| 33 | Deep generative molecular design reshapes drug discovery Xiangxiang Zeng, Fei Wang, Yuan Luo et al. · Cell Reports Medicine | 27 Oct 2022 | 303 |
| 34 | How critical is brain criticality? Jordan O’Byrne, Karim Jerbi · Trends in Neurosciences | 9 Sep 2022 | 286 |
| 35 | Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults Charlene H. Chu, Rune Nyrup, Kathleen Leslie et al. · The Gerontologist | 14 Nov 2021 | 283 |
| 36 | Deep neural networks and mixed integer linear optimization Matteo Fischetti, Jason Jo · Constraints | 26 Apr 2018 | 277 |
| 37 | Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke et al. · Nature Neuroscience | 13 May 2021 | 274 |
| 38 | Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists Hao-Ting Wang, Jonathan Smallwood, Janaı́na Mourão-Miranda et al. · NeuroImage | 8 Apr 2020 | 273 |
| 39 | The default network of the human brain is associated with perceived social isolation R. Nathan Spreng, Emile Dimas, Laetitia Mwilambwe-Tshilobo et al. · Nature Communications | 15 Dec 2020 | 254 |
| 40 | The neuroconnectionist research programme Adrien Doerig, Rowan P. Sommers, Katja Seeliger et al. · Nature reviews. Neuroscience | 30 May 2023 | 249 |
| 41 | Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study Jianzhong Chen, Angela Tam, Valeria Kebets et al. · Nature Communications | 25 Apr 2022 | 249 |
| 42 | The Neurobiology of Social Distance Danilo Bzdok, Robin Dunbar · Trends in Cognitive Sciences | 3 Jun 2020 | 248 |
| 43 | Mapping gene transcription and neurocognition across human neocortex Justine Y. Hansen, Ross D. Markello, Jacob W. Vogel et al. · Nature Human Behaviour | 25 Mar 2021 | 247 |
| 44 | Quantifying the effect of experimental perturbations at single-cell resolution Daniel B. Burkhardt, Jay S. Stanley, Alexander A. Tong et al. · Nature Biotechnology | 8 Feb 2021 | 237 |
| 45 | Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data Philipp Thölke, Yorguin-José Mantilla-Ramos, Hamza Abdelhedi et al. · NeuroImage | 27 Jun 2023 | 225 |
| 46 | MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement Szu‐Wei Fu, Cheng Yu, Tsun-An Hsieh et al. | 27 Aug 2021 | 208 |
| 47 | Quantization and Deployment of Deep Neural Networks on Microcontrollers Pierre-Emmanuel Novac, Ghouthi Boukli Hacene, Alain Pégatoquet et al. · Sensors | 23 Apr 2021 | 208 |
| 48 | Functional specialization within the inferior parietal lobes across cognitive domains Ole Numssen, Danilo Bzdok, Gesa Hartwigsen · eLife | 2 Mar 2021 | 208 |
| 49 | Understanding metric-related pitfalls in image analysis validation Annika Reinke et al. · Nature Methods | Feb 2024 | 206 |
| 50 | Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients András Maifeld et al. · Nature Communications | 30 Mar 2021 | 204 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.