Results 41 to 50 of about 6,254,702 (218)
Many problems in computer vision and recommender systems involve low-rank matrices. In this work, we study the problem of finding the maximum entry of a stochastic low-rank matrix from sequential observations. At each step, a learning agent chooses pairs of row and column arms, and receives the noisy product of their latent values as a reward. The main
Branislav Kveton +5 more
openaire +2 more sources
Low-Rank Optimization With Convex Constraints [PDF]
The problem of low-rank approximation with convex constraints, which appears in data analysis, system identification, model order reduction, low-order controller design and low-complexity modelling is considered. Given a matrix, the objective is to find a low-rank approximation that meets rank and convex constraints, while minimizing the distance to ...
Christian Grussler +2 more
openaire +2 more sources
Supporting Survivor‐Centered Care Through Digital Health Integration
ABSTRACT Survivors of childhood cancer face barriers to receiving guideline‐based, long‐term follow‐up care. Two digital tools, Passport for Care (PFC) and Cancer SurvivorLink (SurvivorLink), address complementary gaps by enabling tailored survivorship care plan (SCP) generation, updating, storage, and sharing.
Jordan G. Marchak +15 more
wiley +1 more source
Locality constrained low-rank sparse learning for object tracking
In this paper, we present a locality constrained low rank sparse learning algorithm for object tracking under the particle filter framework. Locality should be as important as the sparsity.
Fan BJ(范保杰) +3 more
core +1 more source
A unified weight learning and low-rank regression model for robust complex error modeling
One of the most important problems in regression-based error model is modeling the complex representation error caused by various corruptions and environment changes in images. For example, in robust face recognition, images are often affected by varying
Zhou, Jun +2 more
core +1 more source
Sequential low-rank change detection [PDF]
Detecting emergence of a low-rank signal from high-dimensional data is an important problem arising from many applications such as camera surveillance and swarm monitoring using sensors. We consider a procedure based on the largest eigenvalue of the sample covariance matrix over a sliding window to detect the change. To achieve dimensionality reduction,
Yao Xie 0002, Lee M. Seversky
openaire +2 more sources
ABSTRACT Objective To compare the efficacy and safety of roxarestat versus recombinant human erythropoietin (rhEPO) in the management of renal anemia in patients undergoing maintenance hemodialysis. Methods This was a prospective, open‐label, randomized controlled trial.
Lingling Chen, Junjie Zhu, Qiaonan Ge
wiley +1 more source
Low-rank connectivity and state space dynamics.
A: Illustration of recurrent neural network architecture, consisting of inputs and recurrent connectivity. B: Representation of inputs and connectivity in terms of vectors. The input weights form an input vector I.
Lazar Ciric (16793065) +2 more
core +1 more source
Separable and Low-Rank Continuous Games [PDF]
In this paper, we study nonzero-sum separable games, which are continuous games whose payoffs take a sum-of-products form. Included in this subclass are all finite games and polynomial games. We investigate the structure of equilibria in separable games. We show that these games admit finitely supported Nash equilibria.
Noah D. Stein +2 more
openaire +2 more sources
ABSTRACT Background Neuromyelitis optica spectrum disorder (NMOSD) is a relapsing autoimmune disease of the central nervous system. High‐dose intravenous methylprednisolone (IVMP) is the standard first‐line therapy for acute attacks, although some patients remain refractory.
Wataru Horiguchi +5 more
wiley +1 more source

