Results 11 to 20 of about 6,417 (238)
GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Three‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung
Lu Hong +12 more
wiley +1 more source
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel.
Yining Hua +4 more
wiley +1 more source
Stability and generalization of stochastic gradient methods for minimax problems [PDF]
, 2021 Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few.
Yang, Tianbao +3 more
core +3 more sources
Minimax and admissible adaptive two-stage designs in phase II clinical trials
BMC Medical Research Methodology, 2016 Background Simon’s two-stage design is the most widely implemented among multi-stage designs in phase II clinical trials to assess the activity of a new treatment in a single-arm study.
Guogen Shan, Hua Zhang, Tao Jiang
doaj +1 more source
A Spline Smoothing Newton Method for Semi-Infinite Minimax Problems
Journal of Applied Mathematics, 2014 Based on discretization methods for solving semi-infinite programming problems, this paper presents a spline smoothing Newton method for semi-infinite minimax problems. The spline smoothing technique uses a smooth cubic spline instead of max function and
Li Dong, Bo Yu, Yu Xiao
doaj +1 more source
Minimax Optimal Bandits for Heavy Tail Rewards [PDF]
, 2022 Stochastic multiarmed bandits (stochastic MABs) are a problem of sequential decision-making with noisy rewards, where an agent sequentially chooses actions under unknown reward distributions to minimize cumulative regret.
Lim, Sungbin, Lee, Kyungjae
core +1 more source
Infinitely Many Periodic Solutions for Nonautonomous Sublinear Second-Order Hamiltonian Systems
Abstract and Applied Analysis, 2010 Two sequences of distinct periodic solutions for second-order Hamiltonian systems with sublinear nonlinearity are obtained by using the minimax methods.
Peng Zhang, Chun-Lei Tang
doaj +1 more source
Newton-type Methods for Minimax Optimization
CoRR, 2020 Differential games, in particular two-player sequential zero-sum games (a.k.a. minimax optimization), have been an important modeling tool in applied science and received renewed interest in machine learning due to many recent applications, such as adversarial training, generative models and reinforcement learning.
Guojun Zhang +3 more
openaire +2 more sources
Large-scale Minimax Optimization Problems
, 2023 Minimax optimization has long been an important subject in optimization with widespread applications in robust optimization, game theory, and, more recently, in reinforcement learning and Generative Adversarial Networks (GANs). The recent applications of
Saeid Hajizadeh (17091991)
core +1 more source
Periodic Solutions for Subquadratic Discrete Hamiltonian Systems
Advances in Difference Equations, 2007 Some existence conditions of periodic solutions are obtained for a class of nonautono mous subquadratic first-order discrete Hamiltonian systems by the minimax methods in the critical point theory.
Xiaoqing Deng
doaj +2 more sources