Results 31 to 40 of about 124 (120)
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
, 2016 Journal version of previous conference paper appeared at ICML-2016 with same ...
Yao, Quanming, Kwok, James T.
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Bio‐Inspired Optimisation Methods Applied to Low Carbon Power and Energy Problems: A Survey
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Bio‐inspired optimisation methods have been widely applied to complex real‐world problems, particularly in low‐carbon power and energy systems, where optimisation tasks often involve high‐dimensional, constrained and mixed‐integer characteristics.
Tianyu Hu +4 more
wiley +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT A formation inversion algorithm with real‐time performance and accuracy is crucial for natural gamma logging while drilling (LWD). However, traditional inversion algorithms are often limited by high computational resource consumption and insufficient accuracy.
Juntao Liu +4 more
wiley +1 more source
A Quantised Push‐Sum Distributed Adaptive Momentum Algorithm for Optimisation Over Directed Networks
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT In this paper, we investigate a distributed constrained optimisation problem over directed networks. The agents in the networks conduct local computations and communications, endeavouring to collaboratively minimise the aggregation of all locally known convex cost functions subject to a global constraint set.
Qingguo Lü +6 more
wiley +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Generalisation is a crucial aspect of deep learning, enabling models to perform well on unseen data. Currently, most optimisers that improve generalisation typically suffer from efficiency bottlenecks. This paper proposes a double‐integration‐enhanced stochastic gradient descent (DIESGD) optimiser, which treats the negative gradient as an ...
Ting Li +3 more
wiley +1 more source
NEXT: In-Network Nonconvex Optimization
IEEE Transactions on Signal and Information Processing over Networks, 2016 We study nonconvex distributed optimization in multi-agent networks with time-varying (nonsymmetric) connectivity. We introduce the first algorithmic framework for the distributed minimization of the sum of a smooth (possibly nonconvex and nonseparable) function - the agents' sum-utility - plus a convex (possibly nonsmooth and nonseparable) regularizer.
Di Lorenzo Paolo, Scutari Geusaldo
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CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Repetitive motion planning (RMP) for redundant manipulators with high convergent precision becomes an intense research topic due to its more degrees of freedom. In this paper, a specific zeroing neural dynamics (SZND) model for the RMP is first set up via zeroing neurodynamics.
Ying Kong +3 more
wiley +1 more source
Allocative efficiency and the productivity slowdown
Canadian Journal of Economics/Revue canadienne d'économique, EarlyView.Abstract This paper evaluates the contribution of cross‐sector allocative efficiency to the productivity slowdown in the US during the 1970s and 2000s. We extend the framework of Oberfield (2013) to derive sufficient statistics for allocative efficiency and decompose aggregate productivity growth in a multisector economy.
Lin Shao, Rongsheng Tang
wiley +1 more source
Computing Skinning Weights via Convex Duality
Computer Graphics Forum, EarlyView.We present an alternate optimization method to compute bounded biharmonic skinning weights. Our method relies on a dual formulation, which can be optimized with a nonnegative linear least squares setup. Abstract We study the problem of optimising for skinning weights through the lens of convex duality.
J. Solomon, O. Stein
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Fast Injective Mesh Parameterization via Beltrami Coefficient Prolongation
Computer Graphics Forum, EarlyView.Abstract We present a highly efficient and robust method for free boundary injective parameterization of disk‐like triangle meshes with low isometric distortion. Harmonic function–based approaches, grounded in a strong mathematical framework, are widely employed.
G. Fargion, O. Weber
wiley +1 more source