This important study reveals distinct representations of task-related information in the dendrites and somata of cortical neurons during sensorimotor learning and behavioral adaptation. The evidence ...
Abstract: Matrix factorization is a popular approach for large-scale matrix completion. The optimization formulation based on matrix factorization, even with huge size, can be solved very efficiently ...
Abstract: Matrix factorization is a central paradigm in matrix completion and collaborative filtering. Low-rank factorizations have been extremely successful in reconstructing and generalizing ...
As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its ...
Leopard is a fast, modern implementation of sparse, multifrontal symmetric indefinite matrix factorization. It lets you factorize and solve for large sparse matrices much faster than what is possible ...
ABSTRACT: In interactive platforms, we often want to predict which items could be more relevant for users, either based on their previous interactions with the system or their preferences. Such ...
ABSTRACT: Nonnegative matrix factorization (NMF) is a relatively new unsupervised learning algorithm that decomposes a nonnegative data matrix into a parts-based, lower dimensional, linear ...
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