We are excited to see 5 of our papers published at the International Conference on Learning Representations (ICLR) 2023.
ICLR is a top-tier machine learning conference dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. It is well regarded for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, speech recognition, text understanding, gaming, and more.
Borealis AI at ICLR 2023
Borealis AI’s published papers in our product and research focus areas at ICLR include novel algorithms for selective nets and time series analysis, key areas for our product work, and other contributions. The list of Borealis AI’s ICLR papers is below:
These projects and types of work are a team effort. At Borealis AI, we have created a culture of collaboration and a unique combination of factors that help our researchers succeed, including:
- Partnerships: academic and ecosystem partners: MILA, Amii, UBC, and SFU are all represented in our co-authors.
- Borealis AI’s thriving Internship program: Four of the ICLR papers this year were authored by Borealis AI interns.
- Resources: Excellent compute infrastructure that allows researchers to run experiments and conduct the necessary tests.
Research at Borealis AI
Researchers at Borealis AI conduct research in artificial intelligence for financial services. The research team within Borealis AI is large team with backgrounds across artificial intelligence including computer vision, machine learning, and natural language processing, with PhDs in computer science, physics, computational finance, mathematics and more.
In 2023, the team’s commitment to creating real-world impact through scientific pursuit led to Borealis AI establishing research areas of focus – what we call our North Star research – in Asynchronous Temporal Models (ATOM), Non-Cooperative Learning in Competing Markets, and Causal Machine Learning from Observational Data. The latest updates from the ATOM team can be found ATOM: Asynchronous Temporal Models, alongside more information on Borealis AI’s approach to conducting cutting-edge research.
For all the latest research papers, please explore the full research publications library. 📖
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Impressed by the work of the team?
Borealis AI is looking to hire for various roles across different teams. Visit our career page now to find the right role for you and join our team!