Results 101 to 110 of about 295,233 (270)

Stochastic Gradient Descent Algorithm with Multiple Adaptive Learning Rate for Deep Learning

, 2023
Yeong Lin Koay, Hong Seng Sim, Yong Kheng Goh, Sing Yee Chua, Wah June Leong   +4 more
openalex   +1 more source

Wide-Field Telescope Alignment Using the Model-Based Method Combined with the Stochastic Parallel Gradient Descent Algorithm [PDF]

, 2021
Min Li, Ang Zhang, Junbo Zhang, Hao Xian
openalex   +1 more source

Lessons Learned From a Delayed‐Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Freezing of gait (FOG) in people with Parkinson's disease (PwPD) is debilitating and has limited treatments. Modafinil modulates beta/gamma band activity in the pedunculopontine nucleus (PPN), like PPN deep brain stimulation. We therefore tested the hypothesis that Modafinil would improve FOG in PwPD.
Tuhin Virmani, Lakshmi Pillai, Reid D. Landes, Aliyah Glover, Shannon Doerhoff, Jennifer Kleiner, Mitesh Lotia, Rohit Dhall, Edgar Garcia‐Rill   +8 more
wiley   +1 more source

Implicit Bias of Gradient Descent on Linear Convolutional Networks [PDF]

, 2018
Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro   +3 more
openalex   +1 more source

High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers [PDF]

, 2023
Puning Zhao, Zhiguo Wan, Zhang, Pengfei, Ke, Zong, Min, Minghui, Zhao, Puning   +5 more
openalex   +1 more source

Occam Gradient Descent

Deep learning neural network models must be large enough to adapt to their problem domain, while small enough to avoid overfitting training data during gradient descent. To balance these competing demands, over-provisioned deep learning models such as transformers are trained for a single epoch on large data sets, and hence inefficient with both ...
openaire   +2 more sources

Bayesian Estimation Improves Prediction of Outcomes After Epilepsy Surgery

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT We estimated the statistical power of studies predicting seizure freedom after epilepsy surgery. We extracted data from a Cochrane meta‐analysis. The median power across all studies was 14%. Studies with a median sample size or less (n ≤ 56) and a statistically significant result exaggerated the true effect size by a factor of 5.4, while the ...
Adam S. Dickey, Vineet Reddy, Ammar A. Rashied, Nigel P. Pedersen, Robert T. Krafty   +4 more
wiley   +1 more source

Stochastic gradient descent for optimization for nuclear systems. [PDF]

Sci Rep, 2023
Williams A, Walton N, Maryanski A, Bogetic S, Hines W, Sobes V.   +5 more
europepmc   +1 more source

Homogeneous Vector Capsules Enable Adaptive Gradient Descent in\n Convolutional Neural Networks [PDF]

, 2019
Adam Byerly, Tatiana Kalganova
openalex   +1 more source

Cutaneous Phosphorylated Alpha‐Synuclein in Lewy Body Dementia

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To determine the test performance of cutaneous phosphorylated alpha‐synuclein (P‐SYN) in dementia with Lewy bodies (DLB), individuals with reduced Montreal Cognitive Assessment (MoCA) and healthy controls. Methods This is the first subgroup analysis of the Synuclein‐One study, a prospective, blinded study evaluating P‐SYN detection ...
Christopher H. Gibbons, Todd Levine, Charles H. Adler, Bailey Bellaire, Ningshan Wang, Pinky Agarwal, Georgina M. Aldridge, Alexandru Barboi, Daniel Claassen, Virgilio G. H. Evidente, Douglas Galasko, Alejandra Gonzalez‐Duarte, Ramon Gil, Mark Gudesblatt, Stuart H. Isaacson, Horacio Kaufmann, Pravin Khemani, Rajeev Kumar, Guillaume Lamotte, Andy J. Liu, Nikolaus R. McFarland, Mitchell G. Miglis, Adam Reynolds, Gregory A. Sahagian, Marie‐Helene Saint‐Hilaire, Julie B. Schwartzbard, Wolfgang Singer, Michael J. Soileau, Steven Vernino, Patricio Millar Vernetti, Oleg Yerstein, Roy Freeman   +31 more
wiley   +1 more source