Multi-Task Learning is a learning paradigm that uses correlated tasks to
improve performance generalization. A common way to learn multiple tasks is
through the hard parameter sharing approach, in which a single architecture is
used to share the same subset of parameters, creating an inductive bias between
them during the training process. Due to its simplicity, potential to improve
generalization, and reduce computational cost, it has gained the attention of
the scientific and industrial communities. However, tasks often conflict with
each other, which makes it challenging to define how the gradients of multiple
tasks should be combined to allow simultaneous learning. To address this
problem, we use the idea of multi-objective optimization to propose a method
that takes into account temporal behaviour of the gradients to create a dynamic
bias that adjust the importance of each task during the backpropagation. The
result of this method is to give more attention to the tasks that are diverging
or that are not being benefited during the last iterations, allowing to ensure
that the simultaneous learning is heading to the performance maximization of
all tasks. As a result, we empirically show that the proposed method
outperforms the state-of-art approaches on learning conflicting tasks. Unlike
the adopted baselines, our method ensures that all tasks reach good
generalization performances.

采用多目标优化的方法，考虑梯度的时间行为，创建一个动态偏差，利用多任务学习的相关性来提高性能和泛化性能，并确保所有任务达到良好的泛化性能。

利用收敛行为平衡多任务学习中的矛盾任务

Leveraging convergence behavior to balance conflicting tasks in multi-task learning

Intelligent reflecting surface (IRS) has been recently employed to reshape
the wireless channels by controlling individual scattering elements' phase
shifts, namely, passive beamforming. Due to the large size of scattering
elements, the passive beamforming is typically challenged by the high
computational complexity and inexact channel information. In this article, we
focus on machine learning (ML) approaches for performance maximization in
IRS-assisted wireless networks. In general, ML approaches provide enhanced
flexibility and robustness against uncertain information and imprecise
modeling. Practical challenges still remain mainly due to the demand for a
large dataset in offline training and slow convergence in online learning.
These observations motivate us to design a novel optimization-driven ML
framework for IRS-assisted wireless networks, which takes both advantages of
the efficiency in model-based optimization and the robustness in model-free ML
approaches. By splitting the decision variables into two parts, one part is
obtained by the outer-loop ML approach, while the other part is optimized
efficiently by solving an approximate problem. Numerical results verify that
the optimization-driven ML approach can improve both the convergence and the
reward performance compared to conventional model-free learning approaches.

本文主要研究机器学习技术在智能反射表面（IRS）辅助的无线网络中，通过优化驱动的机器学习框架实现性能的最大化，结果表明该方法相比于传统的 “无模型” 学习方法，在收敛性和奖励性能方面都有着优势。

智能反射面辅助无线网络的优化驱动机器学习

Optimization-driven Machine Learning for Intelligent Reflecting Surfaces  Assisted Wireless Networks

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.

通过限制所分配的资源，利用贪婪算法选择最相关的视觉线索，实现了对飞行器实时机动的准确视觉惯性导航。