As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

We propose a visual food recognition framework that integrates the inherent semantic
relationships among fine-grained classes. Our method learns semantics-aware features by …

In this paper, we propose a novel method named JSPNet, to segment 3D point cloud in
semantic and instance simultaneously. First, we analyze the problem in addressing joint …

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to
support large-scale visual recognition (eg, recognizing thousands or even tens of thousands …

To support large-scale visual recognition (ie, recognizing thousands or even tens of
thousands of object classes), a multi-level deep learning algorithm is developed to learn …

In multi-task learning (MTL), multiple related tasks are learned jointly by sharing information
across them. Many MTL algorithms have been proposed to learn the underlying task groups …

We introduce a new anomaly detection methodology for data with latent dependency
structure. As a particular instantiation, we derive a hidden Markov anomaly detector that …

A method includes utilizing two or more classifiers to calculate, for an input image,
probability scores for a plurality of classes based on visual information extracted from the …