The Contextual Attention Block (CAB) is a new plug-and-play module to model context awareness. It is simple and effective and can be integrated with any feed-forward neural network.

CAB infers weights that multiply the feature maps according to their causal influence on the scene, modeling the co-occurrence of different objects in the image.

You can place the CAB module at different bottlenecks to infuse a hierarchical context awareness into the model.

A computationally efficient approach for solving hard nonlinear problems of reinforcement learning (RL). It combines umbrella sampling, from computational physics/chemistry, with optimal control methods. The approach is realized on the basis of neural networks, with the use of policy gradient. It outperforms, by computational efficiency and implementation universality, the available state-of-the-art algorithms, in application to hard RL problems with sparse reward, state traps and lack of terminal states. The proposed approach uses an ensemble of simultaneously acting agents, with a modified reward which includes the ensemble entropy, yielding an optimal exploration-exploitation balance.

Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problems

Connectivity-Inspired Network for Context-Aware Recognition

Stochastic optimization methods have gained significant prominence as effective techniques in contemporary research, addressing complex optimization challenges efficiently. This paper introduces the Parrot Optimizer (PO), an efficient optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots. The study features qualitative analysis and comprehensive experiments to showcase the distinct characteristics of the Parrot Optimizer in handling various optimization problems. Performance evaluation involves benchmarking the proposed PO on 35 functions, encompassing classical cases and problems from the IEEE CEC 2022 test sets, and comparing it with eight popular algorithms. The results vividly highlight the competitive advantages of the PO in terms of its exploratory and exploitative traits. Furthermore, parameter sensitivity experiments explore the adaptability of the proposed PO under varying configurations. The developed PO demonstrates effectiveness and superiority when applied to engineering design problems. To further extend the assessment to real-world applications, we included the application of PO to disease diagnosis and medical image segmentation problems, which are highly relevant and significant in the medical field. In conclusion, the findings substantiate that the PO is a promising and competitive algorithm, surpassing some existing algorithms in the literature. The supplementary files and open source codes of the proposed Parrot Optimizer (PO) is available at https://aliasgharheidari.com/PO.html

Source	Connectivity-Inspired Network for Context-Aware Recognition
Year
Data Source	Papers with Code archive (CC BY-SA)