Search · four archives
Search · four archives
11 papers · ranked by Valyu relevance
Han Cao, Youcheng Zhang, Jan Baumbach, Paul R Burton + 10 more
Multitask learning allows the simultaneous learning of multiple ‘communicating’ algorithms. It is increasingly adopted for biomedical applications, such as the modeling of disease progression. As data protection regulations limit data sharing for such analyses, an implementation of multitask learning on geographically…
Nicholas Menghi, Kemal Kacar, Will Penny
This paper uses constructs from the field of multitask machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the…
Momchil S. Tomov, Eric Schulz, Samuel J. Gershman
The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants’ behavior in a novel two-step decision making task with multiple features and changing reward functions.…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Zhenglong Zhou, Dhairyya Singh, Marlie C. Tandoc, Anna C. Schapiro
Inferring relationships that go beyond our direct experience is essential for understanding our environment. This capacity requires either building representations that directly reflect structure across experiences as we encounter them, or computing the indirect relationships across experiences as the need arises.…
Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete
It is essential to be able to combine datasets across imaging centres to represent the breadth of biological variability present in clinical populations. This, however, leads to two challenges: first, an increase in non-biological variance due to scanner differences, known as the harmonisation problem, and, second…
Shiva Farashahi, Alireza Soltani
Learning appropriate representations of the reward environment is extremely challenging in the real world where there are many options to learn about and these options have many attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
Patrick Q. Zhang, Michael J. Jutras, Adam J.O. Dede, Edgar Y. Walker + 2 more
Adaptive behavior requires maintaining and updating probabilistic beliefs about the world, yet how distributed brain circuits implement such computations remains unknown. We recorded from over 1,400 neurons across six brain regions in monkeys performing a multi-dimensional inference task requiring them to infer hidden…
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…
Lester Melie-Garcia, Bogdan Draganski, John Ashburner, Ferath Kherif
We propose a Multiple Linear Regression (MLR) methodology for the analysis of distributed and Big Data in the framework of the Medical Informatics Platform (MIP) of the Human Brain Project (HBP). MLR is a very versatile model, and is considered one of the workhorses for estimating dependences between clinical…