Regression testing is an essential activity to assure that software code changes do not
adversely affect existing functionalities. With the wide adoption of Continuous Integration …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

摘要网络大数据是指“人, 机, 物” 三元世界在网络空间(Cyberspace) 中交互,
融合所产生并在互联网上可获得的的大数据. 网络大数据的规模和复杂度的增长超出了硬件能力 …

Recommender systems play a vital role in various online services. However, the insulated
nature of training and deploying separately within a specific domain limits their access to …

Image retrieval can be formulated as a ranking problem where the goal is to order database
images by decreasing similarity to the query. Recent deep models for image retrieval have …

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously
challenging due to the fact that it is non-differentiable, and hence cannot be optimised …

We consider the problem of learning deep neural networks (DNNs) for object category
segmentation, where the goal is to label each pixel in an image as being part of a given …

We propose a novel deep metric learning method by revisiting the learning to rank
approach. Our method, named FastAP, optimizes the rank-based Average Precision …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …