Partial multi-label learning (PML), which handles the inaccurate supervision problem where each training instance is associated with a set of candidate labels, naturally arises in many real-world applications due to its effectiveness of reducing the annotation cost significantly. For example, in ...
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Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical
In recent years, machine learning technology has greatly benefited from the development of computing processors and data acquisition methods, expanding its practical applications and improving its effectiveness [1]. Cloud computing platforms provide high-performance computing resources, enabling large-scale da...
(rheumatoid) through serum15, it can recognize most of the cell types found in melanoma cancer16, it can discriminate the anticancer medication reference to their mode of operation for prostate cancer cells17, and it can provide label-free surveillance of therapeutic medicines that is Busulfan and...
Recently, deep supervised hashing methods have shown state-of-the-art performance by integrating feature learning and hash codes learning into an end-to-end network to generate high-quality hash codes. However, it is still a challenge to learn discriminative hash codes for preserving the label inf...
Given the outputs Sx of ψ together with the point coordinates Px, the GMMs are calculated as: \label {eq:gm_3d} \smal \begin {aligned} &\pi ^x_{j} {=} \frac {1}{N_x}\sum _{i=1}\hat {s}^x_{ij}, \bm {\mu }^x_{j} {=} \frac {...
In a Multiprotocol Label Switching (MPLS) network, for example, the interior routers may not contain the full routing table, since they only need to switch packets based on labels, not IP addresses. Embodiments de-couple the default routing from the physical connectivity, removing the requirement...
∪dN that are collected as a single object are trained, but in federated learning, transferring raw data is prohibited. For example, let 𝒴={0,1}Y={0,1} be the label space in the data d. When all the data are collected in the server, each data point x in d has a probability...
Then, the fully connected layer maps these extracted feature vectors to the sample label space and classifies them by constructing a classifier. For the classification, the softmax function is usually selected as the activation function of the fully connected layer, which converts the output vector...