Neural Network Research refers to the pursuit of accurate mathematical characterizations of the electrophysiological properties of individual neurons and interconnected networks, leading to the development of models for pattern recognition and other applications in engineering and medicine. ...
Neural Networks for Identification of Nonlinear Systems: An Overview 2 NEURAL NETWORKS In this section, we give a brief overview of neural networks used in system identification. For a fine collection of key papers in the development of models of neural networks see Neurocomputing: Foundations of ...
因此,设计了一个 SimOTA的替代品TAL。 Task alignment learning:任务对齐学习(TAL),TOOD: Task-aligned One-stage Object Detection任务对齐的单阶段目标检测。由于分类和定位的学习机制不同,两个任务学习到的特征的空间分布可能不同,当使用两个单独的分支进行预测时,会导致一定程度的错位。增加两个任务之间的交互,(2...
image compression and there are multiple modified neural networks that are proposed to perform image compression tasks, however the consequent models are big in size, require high computational power and also best suited for fixed size compression rate and some of them are covered in this survey ...
Optimization is a critical component in deep learning. We think optimization for neural networks is an interesting topic for theoretical research due to va
Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing pl
Distilleris an open-source Python package for neural network compression research. Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides aPyTorchenvironment for prototyping and analyzing compression algorithms, such as spar...
2.1 Learning Neural Networks Let us examine how neural network weights are actually learned. For the logistic sigmoid function, say, fx=11+e−x which if being plotted in a graph would be as shown in Fig. 2. Sign in to download full-size image Fig. 2. Graph of the standard logistic ...
Table 6 shows an overview of the neural networks models. Table 6. Neural Networks Models: An Overview. The first column indicates the category model and the second column the name of each method. Their corresponding acronym is indicated in the first parenthesis and the number of papers counted...
DepthShrinker: Overview To tackle the dilemma betweenthe low hardware utilization of existing efficient DNNsandthe continuously increasing degree of computing parallelism of modern computing platforms, we propose a framework dubbedDepthShrinker, which develops hardware-friendly compact networks via shrinking th...