The accessibility of non-renewable energy sources will diminish to increment popularity and will be depleted in the near future. Along these lines, it is important to track down the substitute fuel to work the vehicle...
详细信息
In this paper, a half-mode substrate integrated waveguide (HMSIW) by using complementary omega slots to provide HMSIW Composite right/left handed (CRLH) Leaky wave antenna (LWA) is presented. Omega shaped slots provid...
详细信息
Deepfake technology has become a significant problem since it allows for the creationof compelling manipulated videos. This research presents a novel hybrid deepfake detection system that combines the Xception and Res...
详细信息
In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorit...
详细信息
Large information datasets often impose an immense number of features where many are found redundant and thus inessential for statistical analysis. In the past, a data preprocessing phase was formalized to cope with t...
详细信息
Decarbonization of transportation is determined to be achieved by designing and deploying cutting-edge electric vehicles (EVs) with low costs and long-distance driving ranges, as well as ensuring their reliability and...
详细信息
This paper focuses on stochastic methods for solving smooth non-convex strongly-concave min-max problems, which have received increasing attention due to their potential applications in deep learning (e.g., deep AUC m...
详细信息
This paper focuses on stochastic methods for solving smooth non-convex strongly-concave min-max problems, which have received increasing attention due to their potential applications in deep learning (e.g., deep AUC maximization, distributionally robust optimization). However, most of the existing algorithms are slow in practice, and their analysis revolves around the convergence to a nearly stationary point. We consider leveraging the Polyak-Łojasiewicz (PL) condition to design faster stochastic algorithms with stronger convergence guarantee. Although PL condition has been utilized for designing many stochastic minimization algorithms, their applications for non-convex min-max optimization remain rare. In this paper, we propose and analyze a generic framework of proximal stage-based method with many well-known stochastic updates embeddable. Fast convergence is established in terms of both the primal objective gap and the duality gap. Compared with existing studies, (i) our analysis is based on a novel Lyapunov function consisting of the primal objective gap and the duality gap of a regularized function, and (ii) the results are more comprehensive with improved rates that have better dependence on the condition number under different assumptions. We also conduct deep and non-deep learning experiments to verify the effectiveness of our methods.
A two stage algorithm developed called Multiple Gain Adaptations for Improved Networks (MGAIN) is presented. MAGAIN alternatively finds output weights and uses several gain factors to update the input weights in a Mul...
详细信息
In this work, a vertically stackable 4F2 memcapacitor crossbar array based on charge trap flash (CTF) is experimentally demonstrated with a TiN-Al2O3- Si3N4-SiO2-Si (TANOS) gate stack. 4-bit multi-level operation of t...
The accelerated move toward adopting the Smart Grid paradigm has resulted in numerous drawbacks as far as security is concerned. Traditional power grids are becoming more vulnerable to cyberattacks as all the control ...
详细信息
暂无评论