We are architects, engineers, scientists and product experts on a mission to revolutionize finance through world-class AI research, solutions and a resilient data platform.
Lumina ⚡️
Powering AI innovation and insights at scale
Lumina is RBC’s internal enterprise data and AI platform, purpose-built to drive innovation at scale. Designed for use by engineers, data scientists, and business analysts, it is a key enabler in the development of leading-edge AI models while ensuring the safety, responsibility and resilience that are at the core of RBC’s approach to AI.
Blog
Minimal LSTMs and GRUs: Simple, Efficient, and Fully Parallelizable
A recent paper generated discussion within the AI community. In this interview, the authors of the paper discuss their motivation, research process, and the interesting findings they discovered.
Bayesian Machine Learning: Function Space
In this (part V) of this series, we investigate the Bayesian approach from the perspective of function space.
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AutoCast++ Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
AutoCast++ Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
T. Sylvain, and J. He.
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ConR: Contrastive Regularizer for Deep Imbalanced Regression
ConR: Contrastive Regularizer for Deep Imbalanced Regression
M. Keramati, L. Meng, and R. David Evans.
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A High-level Overview of Large Language Models
A High-level Overview of Large Language Models
W. Zi, L. El Asri, and S. Prince.
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Top 20 Tutorials for Machine Learning (ML)
Top 20 Tutorials for Machine Learning (ML)
S. Prince.
Learning And Generalization; Natural Language Processing; Reinforcement Learning
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Few-Shot Learning & Meta-Learning | Tutorial
Few-Shot Learning & Meta-Learning | Tutorial
W. Zi, L. S. Ghoraie, and S. Prince.
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RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Y. Gong, G. Mori, and F. Tung. International Conference on Machine Learning (ICML), 2022
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Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate
Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate
*M. Kiarash, H. Zhao, M. Zhai, and F. Tung. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
ATOM: Transforming client interactions into personalized solutions
RBC Borealis’ foundation model for financial services. Our researchers aim to build ML models capable of making inferences from asynchronous event sequences. Asynchronous event sequences are ubiquitous in personal banking applications – from various types of transaction data to client interactions with RBC banking services. ATOM is trained using these datasets, providing it with a breadth of knowledge in financial services.
RESPECT AI™
Responsible AI is key to the future of AI technology, science, development, and adoption. Our RESPECT AI platform contributes knowledge, algorithms, programs, and tooling to help build technology that moves society forward.
ML Research Internships
At RBC Borealis, we are committed to helping advance the next generation of AI experts. Apply your curiosity to a wide variety of theoretical and applied machine learning projects.
Learn moreML Research Internships
At RBC Borealis, we are committed to helping advance the next generation of AI experts. Apply your curiosity to a wide variety of theoretical and applied machine learning projects.
Learn moreCareers
RBC Borealis combines AI research, platform architecture and data engineering to build scalable, resilient solutions for RBC’s clients. Our team impacts millions of lives through scientific advances and brilliant engineering.
Explore open rolesCareers
RBC Borealis combines AI research, platform architecture and data engineering to build scalable, resilient solutions for RBC’s clients. Our team impacts millions of lives through scientific advances and brilliant engineering.
Explore open roles