277 citations · 1.7k across the 70 of their papers we have counts for
5 papers · 1 filter
Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives
Yongxin Yang, Timothy M. Hospedales
Multi-domain learning aims to benefit from simultaneously learning across several different but related domains. In this chapter, we propose a single framework that unifies multi-d…
Gated Neural Networks for Option Pricing: Rationality by Design
Yongxin Yang, Yu Zheng, Timothy M. Hospedales
We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economi…
Trace Norm Regularised Deep Multi-Task Learning
Yongxin Yang, Timothy M. Hospedales
We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is…
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Yongxin Yang, Timothy Hospedales
Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task…
Frankenstein: Learning Deep Face Representations using Small Data
Guosheng Hu, Xiaojiang Peng, Yongxin Yang +2
Deep convolutional neural networks have recently proven extremely effective for difficult face recognition problems in uncontrolled settings. To train such networks, very large tra…