Class (philosophy)
View article: On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper) Open
In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. With the emergence of Machine Learning techniques, such a problem can n…
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Open
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks…
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A Unified Approach to Interpreting Model Predictions Open
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggl…
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition Open
Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power …
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Attention U-Net: Learning Where to Look for the Pancreas Open
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image…
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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation Open
Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the s…
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The Caltech-UCSD Birds-200-2011 Dataset Open
CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The extended version roughly doubles the number of images per category and adds new part localization annotations. All images are annotated with…
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You Only Look Once: Unified, Real-Time Object Detection Open
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associate…
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Deep Eutectic Solvents: A Review of Fundamentals and Applications Open
Deep eutectic solvents (DESs) are an emerging class of mixtures characterized by significant depressions in melting points compared to those of the neat constituent components. These materials are promising for applications as inexpensive …
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Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks Open
Over the last decade, Convolutional Neural Network (CNN) models have been\nhighly successful in solving complex vision problems. However, these deep\nmodels are perceived as "black box" methods considering the lack of\nunderstanding of the…
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Evaluasi Pembelajaran Open
Implementation of learning in the classroom has consequences for a teacher to improve his role and competence, because a competent teacher will find it easier to manage classes and carry out evaluations for his students both individually a…
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Survey on deep learning with class imbalance Open
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent i…
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Deep Learning using Rectified Linear Units (ReLU) Open
We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN). Conventionally, ReLU is used as an activation function in DNNs, with Softmax function as their classification function. Ho…
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The Flipped Classroom: A Survey of the Research Open
The Flipped Classroom: A Survey of the ResearchRecent advances in technology and in ideology have unlocked entirely new directions foreducation research. Mounting pressure from increasing tuition costs and free, online courseofferings is o…
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FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Open
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization…
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Classification assessment methods Open
Classification techniques have been applied to many applications in various fields of sciences. There are several ways of evaluating classification algorithms. The analysis of such metrics and its significance must be interpreted correctly…
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Conditional Image Synthesis With Auxiliary Classifier GANs Open
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We c…
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SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary Open
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its…
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Federated Learning with Non-IID Data Open
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provide…
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KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis Open
Gene set enrichment (GSE) analysis plays an essential role in extracting biological insight from genome-scale experiments. ORA (overrepresentation analysis), FCS (functional class scoring), and PT (pathway topology) approaches are three ge…
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Latent Class Analysis: A Guide to Best Practice Open
Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics. The assumption underlying LCA is that membership in unobserved gr…
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Conditional Image Generation with PixelCNN Decoders Open
This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other netw…
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ClassyFire: automated chemical classification with a comprehensive, computable taxonomy Open
ClassyFire, in combination with ChemOnt (ClassyFire's comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allow…
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NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data Open
Cytotoxic T cells are of central importance in the immune system’s response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the s…
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The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale Open
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adap…
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Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials Open
Density functional theory offers a very accurate way of computing materials\nproperties from first principles. However, it is too expensive for modelling\nlarge-scale molecular systems whose properties are, in contrast, computed using\nint…
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BERTopic: Neural topic modeling with a class-based TF-IDF procedure Open
Topic models can be useful tools to discover latent topics in collections of documents. Recent studies have shown the feasibility of approach topic modeling as a clustering task. We present BERTopic, a topic model that extends this process…
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Is Space-Time Attention All You Need for Video Understanding? Open
We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named "TimeSformer," adapts the standard Transformer architecture to video by enabling spatiotemporal featu…
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Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom Open
Significance Despite active learning being recognized as a superior method of instruction in the classroom, a major recent survey found that most college STEM instructors still choose traditional teaching methods. This article addresses th…
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All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously Open
Variable importance (VI) tools describe how much covariates contribute to a prediction model's accuracy. However, important variables for one well-performing model (for example, a linear model $f(\mathbf{x})=\mathbf{x}^{T}β$ with a fixed c…