Results 151 to 160 of about 28,952 (292)

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm’s convergence properties in non-convex problems.
Mertikopoulos, Panayotis, Kavis, Ali, Cevher, Volkan, Hallak, Nadav   +3 more
core  

Calibrated Stochastic Gradient Descent for Convolutional Neural Networks

, 2019
In stochastic gradient descent (SGD) and its variants, the optimized gradient estimators may be as expensive to compute as the true gradient in many scenarios.
Chen, Chen, Doermann, David, Zhang, Baochang, Zhuo, Li’an, Ye, Qixiang, Liu, Jianzhuang   +5 more
core   +1 more source

Stress History Establishes a Transient Tolerant State That Shapes Antibiotic Survival Upon Resuscitation

Advanced Science, EarlyView.
High‐throughput single‐cell analysis of resuscitating bacteria reveals a starvation‐history‐dependent transiently tolerant subpopulation that survives β$\beta$‐lactam exposure by temporarily reducing growth. Distinct from classical persisters, these actively growing yet dynamically modulated cells dominate survival across clinically relevant antibiotic
Kieran Abbott, Georgeos Hardo, Ruizhe Li, Jack Bradley, Ashraf Zarkan, Somenath Bakshi   +5 more
wiley   +1 more source

An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN). [PDF]

SN Comput Sci, 2023
Vidyashree KP, Rajendra AB.
europepmc   +1 more source

Targeting Supramolecular Active Complexes of Nav1.7/Nav1.8 to Relieve Chronic Neuropathic Pain

Advanced Science, EarlyView.
In mice and patients with severe chronic neuropathic pain (NP), Nav1.7, Nav1.8, TrkB, and five cytoskeletal proteins form supramolecular active complexes (SMACs) with polygonal lattice structures as noxious signal amplifiers in dorsal root ganglion (DRG) neurons.
Liting Sun, Hang Xian, Yunxin Shi, Taotan Yang, Hongyan Shuai, Wenchao Hu, Siying Fei, Miao Xu, Taoyuan Yang, Ruilong Xia, Ting Wen, Fengting Zhu, Yan Fu, Yang Li, Wei Xia, Ran Qian, Yuanying Liu, Zhicheng Tian, Lamei Li, Qian Zhou, Lize Xiong, Rui Cong, Ceng Luo, Shengxi Wu, Xiafeng Shen, Xin Yu, Rou‐Gang Xie, Changgeng Peng   +27 more
wiley   +1 more source

A Novel Sophia-SPGD (Stochastic Parallel Gradient Descent) Optimization Method for Wavefront Correction in WFS-Less AO (Wavefront Sensorless Adaptive Optics) Systems

Photonics
In wavefront sensorless adaptive optics (WFS-less AO) systems, stochastic parallel gradient descent (SPGD) is the primary optimization method for correcting wavefront distortions. However, as the intensity of atmospheric turbulence interference increases,
Peng Chen, Wenjie Yang, Yongqi Ge, Zhiguang Zhang, Xianshuo Li, Zhengqing Qi, Huizhen Yang   +6 more
doaj   +1 more source

Using the Stochastic Gradient Descent Optimization Algorithm on Estimating of Reactivity Ratios. [PDF]

Materials (Basel), 2021
Fazakas-Anca IS, Modrea A, Vlase S.
europepmc   +1 more source

Semi-Stochastic Gradient Descent Methods

, 2013
In this paper we study the problem of minimizing the average of a large number ($n$) of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full ...
Richtárik, Peter, Konečný, Jakub
core   +1 more source

Large‐Scale Genomics Reveals Three‐Source Ancestry and Layered Adaptation to High Altitude in Tibetan Chickens

Advanced Science, EarlyView.
Whole‐genome analysis of 1,054 chickens reveals three ancestral sources (NWC, SYA, and SHF) with distinct temporal entry patterns into the Tibetan Plateau. Route‐specific selection scans, calibrated against a demographic null, suggest complementary functional enrichments—vascular homeostasis (NWC), calcium signaling and cardiac adaptation (SYA), and ...
Zongyi Zhao, Ruixue Nie, Hailu Fan, Tenzin Ngodroup, Li Zhu, Hongbin Pan, Bo Zhang, Hao Zhang   +7 more
wiley   +1 more source

The inverse variance-flatness relation in stochastic gradient descent is critical for finding flat minima. [PDF]

Proc Natl Acad Sci U S A, 2021
Feng Y, Tu Y.
europepmc   +1 more source