Results 131 to 140 of about 142,362 (315)

Stochastic Gradient Descent as Approximate Bayesian Inference

, 2017
35 pages, published version (JMLR 2017)
Mandt, Stephan, Hoffman, Matthew D., Blei, David M.   +2 more
openaire   +3 more sources

Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography [PDF]

, 2022
Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe, Anastasios Kyrillidis   +3 more
openalex   +1 more source

Lattice Structures for Bone Replacement: The Intersection of Bone Biomechanics, Lattice Design, and Additive Manufacturing

Advanced Materials Technologies, EarlyView.
This review outlines how understanding bone's biology, hierarchical architecture, and mechanical anisotropy informs the design of lattice structures that replicate bone morphology and mechanical behavior. Additive manufacturing enables the fabrication of orthopedic implants that incorporate such structures using a range of engineering materials ...
Stylianos Kechagias, Maxwell J. Munford, Frederik C.H. Masure, Richard J. van Arkel, Reece N Oosterbeek   +4 more
wiley   +1 more source

Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction. [PDF]

Micromachines (Basel), 2023
Quan J, Yan B, Sang X, Zhong C, Li H, Qin X, Xiao R, Sun Z, Dong Y, Zhang H.   +9 more
europepmc   +1 more source

Lead‐Free Bismuth Halide Perovskite Memristors: Low‐Voltage Switching and Physical Modeling of Resistive Hysteresis

Advanced Materials Technologies, EarlyView.
Lead‐free bismuth halide perovskite memristors exhibit stable low‐voltage resistive switching behavior. The conductance‐activated quasi‐linear memristor model quantitatively reproduces the experimental hysteresis, confirming ion migration‐driven filament dynamics.
So‐Yeon Kim, Gonzalo Rivera‐Sierra, Bitania Shiferaw Mengesha, Benjamin Iniguez, Juan Bisquert   +4 more
wiley   +1 more source

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. [PDF]

Diagnostics (Basel), 2022
Verma D, Agrawal S, Iwendi C, Sharma B, Bhatia S, Basheer S.   +5 more
europepmc   +1 more source

Bolstering stochastic gradient descent with model building

TOP
AbstractStochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rates are obtained especially when these algorithms are fine-tuned for the application at hand.
Birbil, Ş. İlker, Martin, Özgür, Onay, Gönenç, Öztoprak, Figen   +3 more
openaire   +6 more sources

Generalization Bounds for Label Noise Stochastic Gradient Descent [PDF]

, 2023
Jung Eun Huh, Patrick Rebeschini
openalex   +1 more source

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

Advanced Optical Materials, EarlyView.
Hyperspectral photoluminescence data from graded MoxW1 − xS2 alloys and monolayer MoS2–WS2 lateral heterostructures are analyzed using unsupervised machine learning. The combined use of PCA, t‐SNE, and DBSCAN uncovers distinct excitonic regions that trace how composition, strain, and defects modulate optical responses in these 2D materials.
Maninder Kaur, Nicolas T. Sandino, Jason P. Terry, Mahdi Ghafariasl, Yohannes Abate   +4 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