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 m
For systems that have noisy periods in the input signals, we propose using a data-compression network (Section 5.2) to smooth out the incoming signals before forwarding them to the time-dependent network. The noise can also be handled using a larger window for the input signals entering the ...
由于Anchor-based检测器需要在训练之前进行聚类分析以确定最佳 Anchor 集合,这会一定程度提高检测器的复杂度;同时,在一些边缘端的应用中,需要在硬件之间搬运大量检测结果的步骤,也会带来额外的延时。而 Anchor-free 无锚范式因其泛化能力强,解码逻辑更简单,在近几年中应用比较广泛。经过对 Anchor-free 的实验调研,我...
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...
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 ...
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 ...
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...