Efficient quantum circuits based on the quantum natural gradient by Ananda Roy, Sameer Erramilli, Robert M. Konik

“…Efficient preparation of arbitrary entangled quantum states is crucial for quantum computation. This is particularly important for noisy intermediate-scale quantum simulators relying on variational hybrid quantum-classical algorithms. …”
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Color image steganalysis based on channel gradient correlation by Yuhan Kang, Fenlin Liu, Chunfang Yang, Lingyun Xiang, Xiangyang Luo, Ping Wang

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Stable developmental patterns of gene expression without morphogen gradients. by Maciej Majka, Nils B Becker, Pieter Rein Ten Wolde, Marcin Zagorski, Thomas R Sokolowski

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An Approach for Predicting the Costs of Forwarding Contracts using Gradient Boosting by Haitao Xiao, Yuling Liu, Dan Du, Zhigang Lu

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Diversified gradient boosting ensembles for prediction of the cost of forwarding contracts by Dymitr Ruta, Ming Liu, Ling Cen, Quang Hieu Vu

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Using gradient boosting trees to predict the costs of forwarding contracts by Sławomir Pioroński, Tomasz Górecki

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New Conjugacy Coefficient for Conjugate Gradient Method for Unconstrained Optimization by Hamsa Chilmeran, Huda Najm

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LSGI: interpretable spatial gradient analysis for spatial transcriptomics data by Qingnan Liang, Luisa Solis Soto, Cara Haymaker, Ken Chen

“…We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. …”
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Fake Fingerprint Classification Using Hybrid Features Learning With Gradient Boosting by Muhammad Salman Ali, Arslan Akram, Javed Rashid, Muhammad Arfan Jaffar, Dilawar Shah, Shujaat Ali, Muhammad Tahir

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Estimation of the Conditional Probability Using a Stochastic Gradient Process by Ali Labriji, Abdelkrim Bennar, Mostafa Rachik

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Convergence Rates of Gradient Methods for Convex Optimization in the Space of Measures by Chizat, Lénaïc

“…We study the convergence rate of Bregman gradient methods for convex optimization in the space of measures on a $d$-dimensional manifold. …”
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Dholes Hunting—A Multi-Local Search Algorithm Using Gradient Approximation and Its Application for Blockchain Consensus Problem by Binh Minh Nguyen, Thieu Nguyen, Quoc-Hien Vu, Tran Huy Hung, Tran Hoang Hai, Huynh Thi Thanh Binh, Van-Dang Tran

“…A clustering approach and finding an optimal global algorithm describe primarily based on gradient approximation. We use four benchmark function datasets to evaluate the DhoH’s performance. …”
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Rate distortion optimization for adaptive gradient quantization in federated learning by Guojun Chen, Kaixuan Xie, Wenqiang Luo, Yinfei Xu, Lun Xin, Tiecheng Song, Jing Hu

“…The compression strategy is based on rate distortion theory, which allows us to find an optimal quantization strategy for the gradients. To further reduce the computational complexity, we introduce the Kalman filter into the proposed approach. …”
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Purely Electric‐Driven Field‐Free Magnetization Switching in L10‐FePt Single Film for Reconfigurable Spin Logic Computing by Yuanbo Wang, Jingyan Zhang, Pengwei Dou, Xiao Deng, Yunchi Zhao, Jie Qi, Yi Zhang, Bokai Shao, Jiawang Xu, Xinqi Zheng, He Huang, Yanfei Wu, Baogen Shen, Shouguo Wang

“…This enables the design of purely electrically controlled SOTs‐based logic and in‐memory computing.…”
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Specificity and tunability of efflux pumps: A new role for the proton gradient? by Matthew Gerry, Duncan Kirby, Boian S Alexandrov, Dvira Segal, Anton Zilman

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Gradient Descent-Based Parameter Estimation for Ultra Short Baseline by Yongcheol Kim, Haklim Ko, Hojun Lee

“…Additionally, the localization performance is enhanced using an optimization algorithm based on gradient descent. Through computer simulations and ocean experiments, the proposed method demonstrated up to 29 times better localization performance in computer simulations and up to 107 times better performance in ocean experiments compared to existing methods, verifying that the proposed method outperforms the existing methods.…”
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Global Convergence of a Modified Spectral Conjugate Gradient Method by Huabin Jiang, Songhai Deng, Xiaodong Zheng, Zhong Wan

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Gradient Amplification: An Efficient Way to Train Deep Neural Networks by Sunitha Basodi, Chunyan Ji, Haiping Zhang, Yi Pan

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Stochastic Block-Coordinate Gradient Projection Algorithms for Submodular Maximization by Zhigang Li, Mingchuan Zhang, Junlong Zhu, Ruijuan Zheng, Qikun Zhang, Qingtao Wu

“…Due to high-dimensional data, the computation of the whole gradient vector can become prohibitively expensive. …”
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A Mixed Spectral CD-DY Conjugate Gradient Method by Liu Jinkui, Du Xianglin, Wang Kairong

“…A mixed spectral CD-DY conjugate descent method for solving unconstrained optimization problems is proposed, which combines the advantages of the spectral conjugate gradient method, the CD method, and the DY method. …”
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