19 papers · ranked by Valyu relevance
Guo-Liang Tian, Xuanyu Liu, Yuanfan Zhao
Although the $\textit{expectation-maximization}$ (EM) algorithm is a powerful optimization tool in statistics, it can only be applied to missing/incomplete data problems or to problems with a latent-variable structure. It is well known that the introduction of latent variables (or the data augmentation) is an art…
Bjoern O. Forsberg, Pranav N.M. Shah, Alister Burt
Cryo electron microscopy (cryo-EM) is used by biological research to visualize biomolecular complexes in 3D, but the heterogeneity of cryo-EM reconstructions is not easily estimated. Current processing paradigms nevertheless exert great effort to reduce flexibility and heterogeneity to improve the quality of the…
Sergio Fuentes del Toro, Josue Aranda-Ruiz, Luca Miceli
Surface electromyography (sEMG) is a critical tool for quantifying muscle activity and inferring biomechanical function, enabling the detection of neuromuscular deficits through the analysis of electrical potential propagation. However, the inherent variability in sEMG signal amplitude, influenced by factors such as…
Jonathan Sinclair, Paul John Taylor, Jack Hebron, Darrell Brooks + 2 more
'Howard Thomas Hurst' 'Stephen Atkins'] Electromyography (EMG) is normalized in relation to a reference maximum voluntary contraction (MVC) value. Different normalization techniques are available but the most reliable method for cycling movements is unknown. This study investigated the reliability of different…
Javier Fdez, Nicholas Guttenberg, Olaf Witkowski, Antoine Pasquali
Due to a large number of potential applications, a good deal of effort has been recently made towards creating machine learning models that can recognize evoked emotions from one’s physiological recordings. In particular, researchers are investigating the use of EEG as a low-cost, non-invasive method. However, the poor…
Jimmy Ba, Jamie Kiros, Geoffrey E. Hinton
Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute…
Ewa Roszkowska, Mario Martinelli
Normalization is a critical step in Multiple-Criteria Decision Analysis (MCDA) because it transforms heterogeneous criterion values into comparable information. This study examines normalization techniques through the lens of entropy, highlighting how criterion data structure shapes normalization behavior and ranking…
Kenway Louie
Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral…
Li Chen, Jun Chen
Normalization is the first and a critical step in microbiome sequencing (microbiome-Seq) data analysis to account for variable library sizes. Though RNA-Seq based normalization methods have been adapted for microbiome-Seq data, they fail to consider the unique characteristics of microbiome-Seq data, which contain a…
William Ruth
The EM algorithm is a powerful tool for maximum likelihood estimation with missing data. In practice, the calculations required for the EM algorithm are often intractable. We review numerous methods to circumvent this intractability, all of which are based on Monte Carlo simulation. We focus our attention on the Monte…
Chenguang Lu
The Expectation-Maximization (EM) algorithm for mixture models often results in slow or invalid convergence. The popular convergence proof affirms that the likelihood increases with Q; Q is increasing in the M -step and non-decreasing in the E-step. The author found that (1) Q may and should decrease in some E-steps…
Constantinos Daskalakis, Christos Tzamos, Manolis Zampetakis
The Expectation-Maximization (EM) algorithm is a widely used method for maximum likelihood estimation in models with latent variables. For estimating mixtures of Gaussians, its iteration can be viewed as a soft version of the k-means clustering algorithm. Despite its wide use and applications, there are essentially no…
Rui Xie, Mengrui Zhang, Prahatha Venkatraman, Xinlian Zhang + 9 more
'Gaonan Zhang' 'Robert Carmer' 'Skylar A. Kantola' 'Chi Pui Pang' 'Ping Ma' 'Mingzhi Zhang' 'Wenxuan Zhong' 'Yuk Fai Leung' 'Stephan C.F. Neuhauss'] Many contemporary neuroscience experiments utilize high-throughput approaches to simultaneously collect behavioural data from many animals. The resulting data are often…
Yang Shen, Julia Wang, Saket Navlakha
A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods, such as batch normalization, weight normalization, and their many variants, help to stabilize…
Akshat Gupta, Ahmet Şükrü Özdemir, Gopala Anumanchipalli
Comparative Study with RMSNorm Authors: ['Akshat Gupta' 'Ahmet Şükrü Özdemir' 'Gopala Anumanchipalli'] Layer normalization is a pivotal step in the transformer architecture. This paper delves into the less explored geometric implications of this process, examining how LayerNorm influences the norm and orientation of…
Authors not listed
A white light-emitting hydrogel was synthesized by embedding N,N’-bis(salicylidene)-(2-(3’,4’-diaminophenyl)benzothiazole) (BTS) into a polyethylene oxide-polypropylene oxide-polyethylene oxide (PEO-PPO-PEO, F127) triblock copolymer hydrogel. BTS undergoes excited state intramolecular proton transfer (ESIPT), which…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
Adeleke Maradesa, Baptiste Py, Francesco Ciucci
Electrochemical impedance spectroscopy (EIS) is widely used to study the properties of electrochemical materials and systems. However, analyzing EIS data remains challenging. Among various analysis methods, the distribution of relaxation times (DRT) has emerged as a novel non-parametric approach capable of providing…
Bohan Xu, Rayus Kuplicki, Sandip Sen, Martin P. Paulus
Normative modeling, a group of methods used to quantify an individual’s deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input…