21 papers · ranked by Valyu relevance
Yarin Gal, Zoubin Ghahramani
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a…
Alex Labach, Hojjat Salehinejad, Shahrokh Valaee
Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice. They have been successfully applied in various applications, including neural network regularization, model compression, and in measuring…
Zongjie Ma, Abdul Sattar, Jun Zhou, Qingliang Chen + 1 more
Dropout has proven to be an effective technique for regularization and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability p during the training stage of DNN. Dropout also provides a way of approximately combining exponentially many different neural network…
Jiyang Xie, Zhanyu Ma, Jianjun Lei, Guoqiang Zhang + 3 more
'Zheng‐Hua Tan' 'Jun Guo'] Abstract—Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate overfitting and improve the performance of DNNs. The advanced dropout technique applies a model-free and…
Christian Lee, Zheng Zhang, Skirmantas Janušonis
Random dropout has become a standard regularization technique in artificial neural networks (ANNs), but it is currently unknown whether an analogous mechanism exists in biological neural networks (BioNNs). If it does, its structure is likely to be optimized by hundreds of millions of years of evolution, which may…
Auria Lucia Jiménez-Gutiérrez, Cinthya Ivonne Mota-Hernández, Efrén Mezura-Montes, Rafael Alvarado-Corona
This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and…
Tue Herlau, Morten Mørup, Mikkel N. Schmidt
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grounded in explicit modelling assumptions which so far has precluded its adoption in Bayesian modelling. Using Bayesian entropic reasoning we…
Authors not listed
Acoustic measurements of batteries are known to be correlated to their state-of-charge, creating opportunities for state estimation that do not rely on electrical signals. State estimators are typically parametric models fitted from data, often from the broad toolbox of machine learning. Such models can be easily…
Carter J Sevick, Samantha MaWhinney, Peter L Anderson, Camille M Moore
Longitudinal clinical trials and cohort studies often collect clinical data paired with stored biospecimens. An increasing focus of biomedical research is aimed at leveraging these existing specimens to address new research questions. When a hypothesis of interest proposes to utilize costly, limited or difficult to…
Helai Liu, Mao Mao, Xia Li, Jia Gao + 1 more
Student dropout is a significant social issue with extensive implications for individuals and society, including reduced employability and economic downturns, which, in turn, drastically influence social sustainable development. Identifying students at high risk of dropping out is a major challenge for sustainable…
Hao Tang, Tianle Yue, Ying Li
Machine learning (ML) has become an important technique in materials science, markedly accelerating the discovery and design of novel materials, and concurrently lowering the burden of experimental costs. Uncertainty quantification (UQ) plays a pivotal role in the accurate prediction and innovative design of novel…
Huong Nguyen Thi Cam, Aliza Sarlan, Noreen Izza Arshad, Bilal Alatas
Background Student dropout rates are one of the major concerns of educational institutions because they affect the success and efficacy of them. In order to help students continue their learning and achieve a better future, there is a need to identify the risk of student dropout. However, it is challenging to…
Carlos A. Palacios, José A. Reyes-Suárez, Lorena A. Bearzotti, Víctor Leiva + 2 more
'Víctor Leiva' 'Carolina Marchant' 'Pentti Nieminen'] Data mining is employed to extract useful information and to detect patterns from often large data sets, closely related to knowledge discovery in databases and data science. In this investigation, we formulate models based on machine learning algorithms to extract…
Ismail Elbouknify, Ismail Berrada, Loubna Mekouar, Youssef Iraqi + 4 more
'El Houcine Bergou' 'Hind Belhabib' 'Younes Nail' 'Souhail Wardi'] Student dropout is a global issue influenced by personal, familial, and academic factors, with varying rates across countries. This paper introduces an AI-driven predictive modeling approach to identify students at risk of dropping out using advanced…
Can Huang, Yuqian Jiang, Yuwen Li, Han Zhang + 1 more
Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize…
Kara K. Brower, Catherine Carswell-Crumpton, Sandy Klemm, Bianca Cruz + 4 more
Droplet microfluidics has made large impacts in diverse areas such as enzyme evolution, chemical product screening, polymer engineering, and single-cell analysis. However, while droplet reactions have become increasingly sophisticated, phenotyping droplets by a fluorescent signal and sorting them to isolate…
Elijah Ditchendorf, Isteaque Ahmed, Joseph Sepate, Aashish Priye + 3 more
'Chaoxing Liu' 'Yuqi Chen' 'Zutao Yu'] Molecular tests for infectious diseases and genetic anomalies, which account for significant global morbidity and mortality, are central to nucleic acid analysis. In this study, we present a digital droplet LAMP (ddLAMP) platform that offers a cost-effective and portable solution…
Debajyoti Sinha, Pradyumn Sinha, Ritwik Saha, Sanghamitra Bandyopadhyay + 1 more
DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. It makes ingenious use of structure persevering sampling and modality based principal component selection to rescue minor cell types. Existing implementation of dropClust involves interfacing with…
Aria Trivedi, Thomas Mathew, Matthew Shulman, Lakshmi Thangam + 4 more
A systematic optimization of throughput and operational stability in Sorting by Interfacial Tension (SIFT) is presented. Reducing droplet size and enabling a broader distribution of droplet trajectories increased the number of droplets processed per sorting element, resulting in about a four fold improvement in…
Kun-Lun Guo, Ze-Rui Song, Jia-Le Zhou, Bin Shen + 3 more
Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high freedom in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems with informed of the transient state of droplets and their…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…