Deep multi-task learning attracts much attention in recent years as it
achieves good performance in many applications. Feature learning is important
to deep multi-task learning for sharing common information among tasks. In this
paper, we propose a Hierarchical Graph Neural Network (HGNN) to learn augmented
features for deep multi-task learning. The HGNN consists of two-level graph
neural networks. In the low level, an intra-task graph neural network is
responsible of learning a powerful representation for each data point in a task
by aggregating its neighbors. Based on the learned representation, a task
embedding can be generated for each task in a similar way to max pooling. In
the second level, an inter-task graph neural network updates task embeddings of
all the tasks based on the attention mechanism to model task relations. Then
the task embedding of one task is used to augment the feature representation of
data points in this task. Moreover, for classification tasks, an inter-class
graph neural network is introduced to conduct similar operations on a finer
granularity, i.e., the class level, to generate class embeddings for each class
in all the tasks use class embeddings to augment the feature representation.
The proposed feature augmentation strategy can be used in many deep multi-task
learning models. we analyze the HGNN in terms of training and generalization
losses. Experiments on real-world datastes show the significant performance
improvement when using this strategy.

本文提出了一种基于图神经网络的层级特征增强方法（HGNN），适用于深度多任务学习，通过学习任务之间的共同信息并使用特征增强策略，实现了性能提升。

基于层级图神经网络的深度多任务增强特征学习

Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural  Network

Most existing deep multi-task learning models are based on parameter sharing,
such as hard sharing, hierarchical sharing, and soft sharing. How choosing a
suitable sharing mechanism depends on the relations among the tasks, which is
not easy since it is difficult to understand the underlying shared factors
among these tasks. In this paper, we propose a novel parameter sharing
mechanism, named \emph{Sparse Sharing}. Given multiple tasks, our approach
automatically finds a sparse sharing structure. We start with an
over-parameterized base network, from which each task extracts a subnetwork.
The subnetworks of multiple tasks are partially overlapped and trained in
parallel. We show that both hard sharing and hierarchical sharing can be
formulated as particular instances of the sparse sharing framework. We conduct
extensive experiments on three sequence labeling tasks. Compared with
single-task models and three typical multi-task learning baselines, our
proposed approach achieves consistent improvement while requiring fewer
parameters.

研究深度多任务学习的参数共享机制，提出了一种自动发现稀疏共享结构的方法，利用子网络对多个任务进行部分重叠并行训练，取得了良好的实验效果。

为多个任务学习稀疏共享架构

Learning Sparse Sharing Architectures for Multiple Tasks

Many real-world decision problems are characterized by multiple conflicting
objectives which must be balanced based on their relative importance. In the
dynamic weights setting the relative importance changes over time and
specialized algorithms that deal with such change, such as a tabular
Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are
required. However, this earlier work is not feasible for RL settings that
necessitate the use of function approximators. We generalize across weight
changes and high-dimensional inputs by proposing a multi-objective Q-network
whose outputs are conditioned on the relative importance of objectives and we
introduce Diverse Experience Replay (DER) to counter the inherent
non-stationarity of the Dynamic Weights setting. We perform an extensive
experimental evaluation and compare our methods to adapted algorithms from Deep
Multi-Task/Multi-Objective Reinforcement Learning and show that our proposed
network in combination with DER dominates these adapted algorithms across
weight change scenarios and problem domains.

本文提出了一种多目标 Q 网络，可以处理动态权重下的多目标强化学习问题，并引入了多样性经验回放方法以应对动态权重下的非稳定性问题。实验结果表明，该方法在权重变化场景和问题领域上的性能优于现有的算法。

多目标深度强化学习中的动态权重

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

Face attribute estimation has many potential applications in video
surveillance, face retrieval, and social media. While a number of methods have
been proposed for face attribute estimation, most of them did not explicitly
consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal
and holistic vs. local) during feature representation learning. In this paper,
we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate
multiple heterogeneous attributes from a single face image. In DMTL, we tackle
attribute correlation and heterogeneity with convolutional neural networks
(CNNs) consisting of shared feature learning for all the attributes, and
category-specific feature learning for heterogeneous attributes. We also
introduce an unconstrained face database (LFW+), an extension of public-domain
LFW, with heterogeneous demographic attributes (age, gender, and race) obtained
via crowdsourcing. Experimental results on benchmarks with multiple face
attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed
approach has superior performance compared to state of the art. Finally,
evaluations on a public-domain face database (LAP) with a single attribute show
that the proposed approach has excellent generalization ability.

本文章提出了一种基于深度多任务学习的方法，使用卷积神经网络来估计单张人脸图像中的多个异质属性，并在 MORPH II、LFW + 等数据库上进行了实验，取得了优秀的性能。