In this paper, we study the problem of multi- task metric learning (mtML). We first exam- ine the generalization bound of the regular- ized mtML formulation based on the notion of algorithmic stability, proving the conver- gence rate of mtML and revealing the trade- off between the tasks....
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation
In this section, we delve into the algorithmic foundations that underpin our system's ability to efficiently allocate and execute tasks within smart home environments. Central to our approach is an improved auction-based algorithm tailored for dynamic task allocation among a team of robots. This alg...
and (iii) and (iv) are classification. Importantly, the multi-task learning part of the DNN is aimed to accomplish all of these tasks only by getting the CIC as the input. Task (i) is to ensure the CIC stores much of the information in the original mRNA EP. Due to task (ii), we...
To fill the gaps, this paper proposes a multi-task learning model that simultaneously predicts the probability of LC maneuver, LC risk level, and time-to-lane-change (TTLC), while further analyzing the intrinsic correlation between LC maneuver and LC risk. The model consists of a Convolutional...
We use the Double Deep Q-Network (DDQN) to obtain initially optimized offloading decisions, benefiting from its stability, good convergence, and low algorithmic complexity. Considering the vehicle’s mobility, we design a mobility management policy to correct the initial decisions and obtain the ...
This renders traditional feature matching algorithms ineffective, thereby affecting the accuracy and stability of SLAM point cloud loop closures. Moreover, although SLAM based on deep learning has made progress in depth estimation [3], unsupervised learning methods require explicit ground truth information...
Looking ahead, future endeavors may entail further optimization of reinforcement learning parameters and their application in larger-scale engineering projects. Such endeavors hold the promise of augmenting the algorithm's stability and efficacy, thereby fostering advancements in multitask scheduling...
This renders traditional feature matching algorithms ineffective, thereby affecting the accuracy and stability of SLAM point cloud loop closures. Moreover, although SLAM based on deep learning has made progress in depth estimation [3], unsupervised learning methods require explicit ground truth information...
These patterns underscore the dynamic nature of multi-UAV research, with varying levels of emphasis across algorithmic classes responding to the evolving technological and operational challenges in the field. 5.3. Controlling Paradigm Task assignment approaches can be classified into two main paradigms base...