Results for Time Series
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Training foundation models up to 10x more efficiently with Memory-Mapped Datasets
Training foundation models up to 10x more efficiently with Memory-Mapped Datasets
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DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis
DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis
C.S. Sastry, M. Gilany, K. Y. C. Lui, M. Magill, and A. Pashevich. Transactions on Machine Learning Research (TMLR), 2025
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Closed-form solutions for ODEs
Closed-form solutions for ODEs
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ODEs and SDEs for machine learning
ODEs and SDEs for machine learning
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Introduction to ODEs
Introduction to ODEs
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NeurIPS 2024 Highlights
NeurIPS 2024 Highlights
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Technical Co-op Program
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NeurIPS 2024
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LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
C. Chen, G. Oliveira, H. Sharifi, and T. Sylvain. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024
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Bayesian Neural Networks
Bayesian Neural Networks
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Neural Network Gaussian Processes
Neural Network Gaussian Processes
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ClickHouse Adoption at RBC Borealis
ClickHouse Adoption at RBC Borealis
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Training foundation models up to 10x more efficiently with Memory-Mapped Datasets
Training foundation models up to 10x more efficiently with Memory-Mapped Datasets
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Closed-form solutions for ODEs
Closed-form solutions for ODEs
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ODEs and SDEs for machine learning
ODEs and SDEs for machine learning
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Introduction to ODEs
Introduction to ODEs
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Bayesian Neural Networks
Bayesian Neural Networks
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Neural Network Gaussian Processes
Neural Network Gaussian Processes
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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
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DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis
DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis
C.S. Sastry, M. Gilany, K. Y. C. Lui, M. Magill, and A. Pashevich. Transactions on Machine Learning Research (TMLR), 2025
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LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
C. Chen, G. Oliveira, H. Sharifi, and T. Sylvain. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024
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Conditional Diffusion Models as Self-supervised Learning Backbone for Irregular Time Series
Conditional Diffusion Models as Self-supervised Learning Backbone for Irregular Time Series
Hamed Shirzad, R. Deng, H. Zhao, and F. Tung. Workshop at International Conference on Representation Learning (ICLR), 2024
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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
Q. Yan, R. Seraj, J. He, L. Meng, and T. Sylvain. International Conference on Learning Representations (ICLR), 2024
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What Constitutes Good Contrastive Learning in Time-Series Forecasting?
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
C. Zhang, Q. Yan, L. Meng, and T. Sylvain. Workshop at International Joint Conference on Artificial Intelligence (IJCAI), 2023
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Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation
Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation
A. Hajimoradlou, L. Pishdad, F. Tung, and M. Karpusha. Workshop at International Conference on Machine Learning (ICML), 2022
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Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
M. Amin Shabani, A. Abdi, L. Meng, and T. Sylvain. International Conference on Learning Representations (ICLR), 2023
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Continuous Latent Process Flows
Continuous Latent Process Flows
R. Deng, M. Brubaker, G. Mori, and A. Lehrmann. Conference on Neural Information Processing Systems (NeurIPS), 2021
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Learning Discriminative Prototypes with Dynamic Time Warping
Learning Discriminative Prototypes with Dynamic Time Warping
X. Chang, F. Tung, and G. Mori. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2021
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Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
R. Deng, B. Chang, M. Brubaker, G. Mori, and A. Lehrmann. Conference on Neural Information Processing Systems (NeurIPS), 2020
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NeurIPS 2024 Highlights
NeurIPS 2024 Highlights
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ClickHouse Adoption at RBC Borealis
ClickHouse Adoption at RBC Borealis
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Inspiring Impact and Innovation: Let's SOLVE it Presentations Day Fall 2023
Inspiring Impact and Innovation: Let's SOLVE it Presentations Day Fall 2023
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Unlocking Potential: Get to Know RBC Borealis's Fall 2023 Research Interns and Engineering Co-op Students
Unlocking Potential: Get to Know RBC Borealis's Fall 2023 Research Interns and Engineering Co-op Students
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RBC Wins Best Use of AI for Customer Experience for NOMI Forecast
RBC Wins Best Use of AI for Customer Experience for NOMI Forecast
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RBC Capital Markets announces the launch of Aiden® Arrival, the second algorithm on the Aiden® platform
RBC Capital Markets announces the launch of Aiden® Arrival, the second algorithm on the Aiden® platform
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A Day in the Life of a Product Manager - RBC Borealis
A Day in the Life of a Product Manager - RBC Borealis
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Support & Maintenance … without the ‘hand over’
Support & Maintenance … without the ‘hand over’
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The (never-ending) production stage
The (never-ending) production stage
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Designing machine learning for human users
Designing machine learning for human users