Normalization (sociology)
View article: ImageNet classification with deep convolutional neural networks
ImageNet classification with deep convolutional neural networks Open
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%…
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Open
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates …
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Instance Normalization: The Missing Ingredient for Fast Stylization Open
It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is l…
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Deep learning with convolutional neural networks for EEG decoding and visualization Open
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG an…
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Spectral Normalization for Generative Adversarial Networks Open
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discr…
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Self-Attention Generative Adversarial Networks Open
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details a…
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Normalization and microbial differential abundance strategies depend upon data characteristics Open
These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.
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Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R Open
Motivation Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts…
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A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor Open
Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data co…
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Spectral Normalization for Generative Adversarial Networks Open
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discr…
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On Calibration of Modern Neural Networks Open
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike tho…
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Neuromodulatory Control Networks (NCNs): A Biologically Inspired Architecture for Dynamic LLM Processing Open
Large Language Models (LLMs) based on the Transformer architecture have achieved remarkable success, yet their core processing mechanisms remain largely static after training. While powerful, this static nature limits their ability to dyna…
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Advanced Normalization Tools (ANTs) Open
See ANTs GitHub https://github.com/ANTsX/ANTs#readme
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks Open
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant ar…
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Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification Open
Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep …
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Tolerance limits and methodologies for<span>IMRT</span>measurement‐based verification<span>QA</span>:<i>Recommendations of<span>AAPM</span>Task Group No. 218</i> Open
Purpose Patient‐specific IMRT QA measurements are important components of processes designed to identify discrepancies between calculated and delivered radiation doses. Discrepancy tolerance limits are neither well defined nor consistently…
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Single-Cell RNA-Seq Technologies and Related Computational Data Analysis Open
Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these meth…
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MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites Open
Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality …
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Open
We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning…
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Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts Open
The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data…
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Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi Open
Summary The minfi package is widely used for analyzing Illumina DNA methylation array data. Here we describe modifications to the minfi package required to support the HumanMethylationEPIC (‘EPIC’) array from Illumina. We discuss methods f…
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A step-by-step workflow for low-level analysis of single-cell RNA-seq data Open
Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data co…
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LSTM-CNN Architecture for Human Activity Recognition Open
In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity an…
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Using Normalization Process Theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review Open
BackgroundNormalization Process Theory (NPT) identifies, characterises and explains key mechanisms that promote and inhibit the implementation, embedding and integration of new health techniques, technologies and other complex intervention…
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Visualizing the Loss Landscape of Neural Nets Open
Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and …
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DeepSynergy: predicting anti-cancer drug synergy with Deep Learning Open
Motivation While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerge…
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Learning Deep Transformer Models for Machine Translation Open
Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto stan…
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Direct Training for Spiking Neural Networks: Faster, Larger, Better Open
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive performance compared with artificial neural networks (ANNs),…
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Ordered quantile normalization: a semiparametric transformation built for the cross-validation era Open
Normalization transformations have recently experienced a resurgence in popularity in the era of machine learning, particularly in data preprocessing. However, the classical methods that can be adapted to cross-validation are not always ef…
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Self-Normalizing Neural Networks Open
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FN…