Many real-world pattern recognition problems can be modeled using multiple-instance learning (MIL), where instances are grouped into bags, and each bag is assigned a label. State-of-the-art MIL methods provide a high level of performance when strong assumptions are made regarding the underlying data distributions, and the proportion of positive to negative instances in positive bags. In this pa...

We propose a generative Bayesian model that predicts instance labels from weak (bag-level) supervision. We solve this problem by simultaneously modeling class distributions by Gaussian mixture models and inferring the class labels of positive bag instances that satisfy the multiple instance constraints. We employ Dirichlet process priors on mixture weights to automate model selection, and effic...

Normally, when we notice a change taking place, our conscious experience has a corresponding quality of phenomenal change. Here it is argued that one’s experience can have this quality at or during a time when there is no change in which phenomenal properties one instantiates. This undermines a number of otherwise forceful arguments against leading metaphysical theories of change, but also requ...

In multi-instance multi-label (MIML) learning, datasets are given in the form of bags, each of which contains multiple instances and is associated with multiple labels. This paper considers a novel instance clustering problem in MIML learning, where the bag labels are used as background knowledge to help group instances into clusters. The goal is to recover the class labels or to find the subcl...

Electric power operation is necessary for the development of grid companies, where safety monitoring electric difficult. Irregular deformable objects commonly used in electrical construction, such as belts and seines, have a dynamic geometric appearance which leads to poor performance traditional detection methods. This paper proposes an end-to-end instance segmentation method using multi-insta...