To handle possible misspecification of the structure, we propose a method named Adaptive and Robust MUlti-Task Learning (ARMUL):min Θ ∈ R d × m , Γ ∈ Ω { ∑ j = 1 m w j [ f j ( θ j ) + λ j ‖ θ j − γ j ‖ 2 ] } ....
Robust multi-task learning with {\textless}i{\textgreater}t{\textless}/i{\textgreater}-processesYu, ShipengTresp, VolkerYu, Kai
Multi-task learningIn this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated ...
aware Network (MPN), which is designed to extract semantically aligned part-level features from pedestrian images. MPN solvesthe body part misalignment problem via multi-task learning (MTL) in the training stage. More specif i cally, it builds one main task (MT)and one auxiliary task (AT) ...
原来的题目Federated Multi-Task Learning for Competing Constraints 关于作者 这篇论文的作者在17年就挖了“联邦多任务学习”这个坑,在这四年里一直在做关于这方面的内容,感觉值得学习。不能不停挖新坑,应该找对一个坑深挖。 摘要 首先介绍一下联邦学习中公平性和鲁棒性的定义。
the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of a prior knowledge on variable structure. To tackle this problem, we propose a new class of a...
与常见的multi-task RL的设计不同,一般来说是给每一个任务赋予一个one-hot编码,然后为神经网络设计一个task encoder,用来将one-hot编码映射为一个连续空间表征向量e。但是在TD-MPC2中,e是一个神经网络参数,在训练过程中通过梯度下降和其他所有模型一同完成训练,但他被限制为二范数小于等于1(位于一个高维球内),...
the proposed algorithm will perfectly recover the underlying "true" similarity matrix with a high probability. Our results show that the new task clustering method can discover task clusters for training flexible and superior neural network models in a multi-task learning setup for sentiment ...
Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing methods use an adaptive weight updating scheme, where task weights are dynamically adjusted based on their respective losses...
To address the former, we use a multi-task learning framework with ELECTRA, a recently proposed improvement on BERT, that has a generator-discriminator structure. The generator allows us to inject errors into the training data and, as our experiments show, this improves robustness against speech ...