Background Joint analyses across multiple health datasets can increase statistical power and improve the generalisability of research findings. However, limitations on data sharing often prevent ...
Vasilis Kontonis, Yuchen Zeng, Shivam Garg, Lingjiao Chen, Hao Tang, Ziyan Wang, Ahmed Awadallah, Eric Horvitz, John Langford, Dimitris Papailiopoulos We taught models to compress their own ...
Adjunctive low-level laser therapy (LLLT) is proposed to improve periodontal healing post-SRP, but results are inconclusive and mostly reported as group averages. There's a need for decision tools to ...
Financial markets must assess how valuable a company's innovations are, but this is difficult. Patents contain rich information about innovation quality, but extracting meaningful signals from complex ...
A powerful and flexible Python library designed to simplify the training and fine-tuning of modern foundation models on tabular data. Provides a high-level, scikit-learn-compatible API that abstracts ...
A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...
Data Science combines scientific inquiry, statistical knowledge and computer programming with a focus on learning powerful insights from big data. Businesses use data to plan, evaluate, innovate, and ...
Many companies are rushing to incorporate AI into their business models without being able to accurately gauge its benefits. Applying the principles of causal inference takes away the guesswork. The ...
In an era where data-driven decision-making dominates the business landscape, traditional AI has excelled at predicting outcomes based on past occurrences. Yet, as our challenges grow in complexity, ...
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