Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. A plethora of deep learning methods have been proposed that generally outperform other machine learning solutions. Our review primarily discusses algorithms that were published in mainstream con...
This allows the agent to explore persistently without modifying the greedy policy. 3 Background In this section, we introduce the notations and recall the basic formulation of the actor-critic framework in deep reinforcement learning. 3.1 Problem formulation For any set \({\mathcal {X}}\), \...
A directed graph is an ordered pair G=(V,E), where E⊂V×V. The elements of V are called vertices, nodes, or points, and the elements of E are called arrows, directed edges, or directed arcs. Definition 27 A state portrait (of dimension n) is a directed graph GH=(Bn,EH), wh...
Most existing evaluation rubrics focus on assessing human-authored note quality, and they do not encompass all the elements required to evaluate the unique aspects of LLM-generated outputs6,7,8. Pre-LLM automated evaluations Automated metrics offer a practical solution to the resource constraints of...
Essentially, it employs a greedy algorithm that examines the detection boxes individually. For each detection box, the algorithm finds the unmatched ground-truth box with the highest Intersection over Union (IoU) score. If the IoU exceeds a threshold, a match is established, and the corresponding...
Three elements of deep learning [28] Full size image For computing power, AI computing has followed several trends. Firstly, the widespread adoption of specialized hardware accelerators such as GPUs and Tensor Processing Units (TPUs) has significantly improved the computational efficiency of tasks. Se...
The RSSD may use the greedy algorithm to select the candidate flash block who has the least number of valid pages. The garbage collection procedure of the RSSD follows the Algorithm 1. The RSSD may utilize idle I/O cycles to transfer compressed stale data and RTT blocks to the remote cloud...
The key advantages of the ACO method include the implementation of feedback, distributed computing and greedy heuristics. The positive feedback enables rapid detection of suitable solutions. The distributed computations disrupt the convergence, and the greedy algorithm offers a suitable solution early on...
These difficulties arise mainly from the significant range of parameter or algorithm choices involved when using this type of approach and the lack of guidance as to how to select them. In addition, the scientific community's level of understanding of why different heuristics work effectively (or ...
An important feature of RBFNs is the ex- istence of a fast, linear learning algorithm in a network Introduction capable of representing complex non-linear mapping. At the same time it is also important to improve the gener- Multi-layer perceptron (MLP) network models are the pop- ular ...