The data sets available to train models for in silico drug discovery efforts are often small.
Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence …

Abstract Machine learning and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …

Racial equality is an important theme of international human rights law, but it has been
largely obscured when the overall face recognition accuracy is pursued blindly. More facts …

The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

In this paper, we address the problem of unsupervised domain transfer learning in which no
labels are available in the target domain. We use a transformation matrix to transfer both the …

Although deep face recognition has achieved impressive progress in recent years,
controversy has arisen regarding discrimination based on skin tone, questioning their …

In the unsupervised open set domain adaptation (UOSDA), the target domain contains
unknown classes that are not observed in the source domain. Researchers in this area aim …

A prevailing problem in many machine learning tasks is that the training (ie, source domain)
and test data (ie, target domain) have different distribution [ie, non-independent identical …

Subspace learning and reconstruction have been widely explored in recent transfer learning
work. Generally, a specially designed projection and reconstruction transfer functions …

Early toxicity assessment plays a vital role in the drug discovery process on account of its
significant influence on the attrition rate of candidates. Recently, constant upgrading of …