Suchergebnisse - linear-time gradient algorithm

Gradients Do Grow on Trees: A Linear-Time O(N)-Dimensional Gradient for Statistical Phylogenetics von Ji, Xiang, Zhang, Zhenyu, Holbrook, Andrew, Nishimura, Akihiko, Baele, Guy, Rambaut, Andrew, Lemey, Philippe, Suchard, Marc A

“… Even worse is its gradient evaluation, often used to target regions of high probability. Order O(N)-dimensional gradient calculations based on the standard pruning algorithm require O …”
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Boundary Conditions for Linear Exit Time Gradient Trajectories Around Saddle Points: Analysis and Algorithm von Dixit, Rishabh, Gurbuzbalaban, Mert, Bajwa, Waheed U.

“… in linear time, explores the contractive and expansive …”
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Gain-loss-duplication models for copy number evolution on a phylogeny: Exact algorithms for computing the likelihood and its gradient von Csűrös, Miklós

“… Gene gain-loss-duplication models are commonly based on continuous-time birth–death processes. Employed in a phylogenetic context, such models have been …”
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Linear Model and Gradient Feature Elimination Algorithm Based on Seasonal Decomposition for Time Series Forecasting von Cheng, Sheng-Tzong, Lyu, Ya-Jin, Lin, Yi-Hong

“… This study proposes a linear time series model architecture based on seasonal decomposition …”
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Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models von Shao, Yucai, Magee, Andrew F., Vasylyeva, Tetyana I., Suchard, Marc A.

“… We present here a linear-time algorithm to compute the gradient of the birth-death …”
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Scalable Bayesian Divergence Time Estimation With Ratio Transformations von Ji, Xiang, Fisher, Alexander A, Su, Shuo, Thorne, Jeffrey L, Potter, Barney, Lemey, Philippe, Baele, Guy, Suchard, Marc A

“… To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios …”
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A linear-time algorithm for the trust region subproblem based on hidden convexity von Wang, Jiulin, Xia, Yong

“… ). The total time complexity is less than that of the recent linear-time algorithm. The algorithm is further extended to the two-sided trust region subproblem …”
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Fast predictive control of linear, time-invariant systems using an algorithm based on the fast gradient method and augmented Lagrange multipliers von Kogel, M., Findeisen, R.

“… We present an algorithm based on the fast gradient method and augmented Lagrange multipliers for model predictive control of linear, discrete-time, time-invariant, systems with constraints …”
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US Patent Issued to Bernafon on April 1 for "Method for monitoring the influence of ambient noise on stochastic gradient algorithms during identification of linear time-invariant systems" (Swiss Inventors)

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Control of linear time-varying systems by the gradient algorithm von Min-Shin Chen

“… A control design is proposed for a linear time-varying system which exhibits sustained but bounded state oscillations …”
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Combined state and multi-innovation parameter estimation for an input non-linear state-space system using the key term separation von Wang, Xuehai, Ding, Feng, Hayat, Tasawar, Alsaedi, Ahmed

“… In this study, the authors study the state and parameter estimation problem for an input non-linear system consisting of a static non-linear block and a linear time-invariant state space subsystem …”
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Boundary Conditions for Linear Exit Time Gradient Trajectories Around Saddle Points: Analysis and Algorithm von Dixit, Rishabh, Gurbuzbalaban, Mert, Bajwa, Waheed U

“… in linear time, explores the contractive and expansive …”
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Attributed network embedding via subspace discovery von Zhang, Daokun, Yin, Jie, Zhu, Xingquan, Zhang, Chengqi

“… While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt …”
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Frequency-Domain Analysis of Distributed Optimization: Fundamental Convergence Rate and Optimal Algorithm Synthesis von Zhang, Shiqi, Wu, Wuwei, Li, Zhongkui, Chen, Jie, Georgiou, Tryphon T.

“… We propose a general class of gradient-based distributed algorithms that can be viewed as interconnected dynamical systems consisting of a linear time-invariant subsystem and a Lur'e-type nonlinear …”
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On Topological Classification of Gradient-like Flows on an $$n$$-sphere in the Sense of Topological Conjugacy von Kruglov, Vladislav E., Malyshev, Dmitry S., Pochinka, Olga V., Shubin, Danila D.

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Prediction of Mixed-mode I and II effective fracture toughness of several types of concrete using the extreme gradient boosting method and metaheuristic optimization algorithms von Fakhri, Danial, Khodayari, Ahmadreza, Mahmoodzadeh, Arsalan, Hosseini, Mehdi, Hashim Ibrahim, Hawkar, Hussein Mohammed, Adil

“… •Development of XGBoost-based metaheuristic algorithms to predict the effective toughness (K_eff) of concrete.•Using …”
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Adachi-Like Chaotic Neural Networks Requiring Linear-Time Computations by Enforcing a Tree-Shaped Topology von Ke Qin, Oommen, B.J.

“… The Adachi neural network (AdNN) is a fascinating neural network (NN) which has been shown to possess chaotic properties, and to also demonstrate associative …”
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Supervised factor modeling for high-dimensional linear time series von Huang, Feiqing, Lu, Kexin, Zheng, Yao, Li, Guodong

“… -dimensional linear time series. Its non-asymptotic properties are discussed by balancing the estimation, approximation and truncation errors …”
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Characterizing and correcting gradient errors in non-cartesian imaging: Are gradient errors linear time-invariant (LTI)? von Brodsky, Ethan K., Samsonov, Alexey A., Block, Walter F.

“… Non‐Cartesian and rapid imaging sequences are more sensitive to scanner imperfections such as gradient delays and eddy currents …”
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Performance Analysis of an Optical System for FSO Communications Utilizing Combined Stochastic Gradient Descent Optimization Algorithm von Galaktionov, Ilya, Toporovsky, Vladimir

“… The algorithm we developed exhibits linear time and space complexity and demonstrates low sensitivity to variations in input parameters …”
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