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 …

A number of computer vision problems such as human age estimation, crowd density
estimation and body/face pose (view angle) estimation can be formulated as a regression …

Birgé and Massart proposed in 2001 the slope heuristics as a way to choose optimally from
data an unknown multiplicative constant in front of a penalty. It is built upon the notion of …

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

Multistep-ahead forecasts can either be produced recursively by iterating a one-step-ahead
time series model or directly by estimating a separate model for each forecast horizon. In …

We propose a public key watermarking algorithm for image integrity verification. This
watermark is capable of detecting any change made to an image, including changes in pixel …

The classical fuzzy system modeling methods implicitly assume data generated from a
single task, which is essentially not in accordance with many practical scenarios where data …

Multi-task learning (MTL) methods have shown promising performance by learning multiple
relevant tasks simultaneously, which exploits to share useful information across relevant …

How much electricity is going to be consumed for the next 24 hours? What will be the
temperature for the next three days? What will be the number of sales of a certain product for …

The classical fuzzy system modeling methods have been typically developed for the single
task modeling scene, which is essentially not in accordance with many practical applications …