-
ODEs and SDEs for machine learning
ODEs and SDEs for machine learning
-
Minimal LSTMs and GRUs: Simple, Efficient, and Fully Parallelizable
Minimal LSTMs and GRUs: Simple, Efficient, and Fully Parallelizable
-
Infinite-Width Networks from Different Viewpoints: A Comprehensive Collection of Research Tutorials
Infinite-Width Networks from Different Viewpoints: A Comprehensive Collection of Research Tutorials
-
Bayesian Neural Networks
Bayesian Neural Networks
-
Neural Network Gaussian Processes
Neural Network Gaussian Processes
-
Pre-training multi-billion parameter LLMs on a single GPU with Flora
Pre-training multi-billion parameter LLMs on a single GPU with Flora
-
AutoCast++ Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
AutoCast++ Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
-
Tree Cross Attention
Tree Cross Attention
-
ConR: Contrastive Regularizer for Deep Imbalanced Regression
ConR: Contrastive Regularizer for Deep Imbalanced Regression
-
Uncertainty Herding: One Active Learning Method for All Label Budgets
Uncertainty Herding: One Active Learning Method for All Label Budgets
W. Bae, *D. J. Sutherland, and *G. Oliveira. International Conference on Learning Representations (ICLR), 2025
-
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
Y. Wen, B. Shayegh, C. Huang, Y. Cao, and L. Mou. Association for the Advancement of Artificial Intelligence (AAAI), 2025
-
Self-Supervised Pretext Tasks for Event Sequence Data from Detecting Misalignment
Self-Supervised Pretext Tasks for Event Sequence Data from Detecting Misalignment
Y. Wang, H. Zhao, R. Deng, F. Tung, and G. Mori. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024
-
Unsupervised Event Outlier Detection in Continuous Time
Unsupervised Event Outlier Detection in Continuous Time
S. Nath, K. Y. C. Lui, and S. Liu. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024
-
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
-
Inference, Fast and Slow: Reinterpreting VAEs for OOD Detection
Inference, Fast and Slow: Reinterpreting VAEs for OOD Detection
S. Huang, J. He, and K. Y. C. Lui. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024
-
Do LLMs Build World Representations? Probing Through the Lens of State Abstraction
Do LLMs Build World Representations? Probing Through the Lens of State Abstraction
Z. Li, Y. Cao, and J. C.K. Cheung. Conference on Neural Information Processing Systems (NeurIPS), 2024
-
NeuZip: Memory-Efficient Training and Inference with Dynamic Compression of Neural Networks
NeuZip: Memory-Efficient Training and Inference with Dynamic Compression of Neural Networks
Y. Hao, Y. Cao, and L. Mou. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024
-
ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models
ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models
W. Pang, M. Shafieinejad, L. Liu, S. Hazlewood, and X. He. Conference on Neural Information Processing Systems (NeurIPS), 2024
-
Identifying and Addressing Delusions for Target-Directed Decision-Making
Identifying and Addressing Delusions for Target-Directed Decision-Making
M. Zhao, T. Sylvain, D. Precup, and Y. Bengio. Workshop at Conference on Neural Information Processing System (NeurIPS), 2024
-
Leveraging Environment Interaction for Automated PDDL Generation and Planning with Large Language Models
Leveraging Environment Interaction for Automated PDDL Generation and Planning with Large Language Models
S. Mahdavi, R. Aoki, K. Tang, and Y. Cao. Conference on Neural Information Processing System (NeurIPS), 2024
-
Jump Starting Bandits with LLM-Generated Prior Knowledge
Jump Starting Bandits with LLM-Generated Prior Knowledge
P. A. Alamdari, Y. Cao, and K. Wilson. Conference on Empirical Methods in Natural Language Processing, 2024
-
Tackling real-world issues: Let’s SOLVE it Presentations Day Spring 2024
Tackling real-world issues: Let’s SOLVE it Presentations Day Spring 2024
-
NeurIPS 2024 Highlights
NeurIPS 2024 Highlights
-
2023-2024 RBC Borealis Fellowships Award Ceremony: an Evening to Remember
2023-2024 RBC Borealis Fellowships Award Ceremony: an Evening to Remember
-
Celebrating the Future of AI: Meet the 2023-2024 RBC Borealis Fellows
Celebrating the Future of AI: Meet the 2023-2024 RBC Borealis Fellows
-
RBC maintains strong AI leadership position in financial services
RBC maintains strong AI leadership position in financial services
-
Unveiling RBC Borealis: Driving innovation in AI & data
Unveiling RBC Borealis: Driving innovation in AI & data
-
ClickHouse Adoption at RBC Borealis
ClickHouse Adoption at RBC Borealis
-
Supporting Emerging Tech Talent: RBC Borealis at the DLRL Summer School
Supporting Emerging Tech Talent: RBC Borealis at the DLRL Summer School
-
Leading in artificial intelligence through education
Leading in artificial intelligence through education
-
RBC Borealis at ICLR 2024
RBC Borealis at ICLR 2024
-
Inspiring Impact and Innovation: Let's SOLVE it Presentations Day Fall 2023
Inspiring Impact and Innovation: Let's SOLVE it Presentations Day Fall 2023