Matching (statistics)
View article: A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations Open
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. …
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The Concept of Fit in Strategy Research: Towards Verbal and Statistical Correspondence Open
Strategic management relationships are increasingly specified by invoking a general conceptualization of fit ' (alternately termed as contingency, congruency, coalignment, consistency, etc.), but with inadequate correspondence between theo…
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Why Propensity Scores Should Not Be Used for Matching Open
We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus increasing imbalance, inefficiency, model dependence, and bias…
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Enhanced-alignment Measure for Binary Foreground Map Evaluation Open
The existing binary foreground map (FM) measures address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-level information independently, while cognitive vision studies ha…
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Learning deep representations by mutual information estimation and maximization Open
In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about…
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A simple framework for contrastive learning of visual representations Open
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. …
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Color Balance and Fusion for Underwater Image Enhancement Open
We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the…
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Siamese Instance Search for Tracking Open
In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art trac…
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Worst-Case Analysis of a New Heuristic for the Travelling Salesman Problem Open
An O( n 3 ) heuristic algorithm is described for solving d -city travelling salesman problems (TSP) whose cost matrix satisfies the triangularity condition. The algorithm involves as substeps the computation of a shortest spanning tree of …
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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Open
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most med…
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Generative Modeling by Estimating Gradients of the Data Distribution Open
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on…
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Matching Networks for One Shot Learning Open
Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for l…
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Training Generative Adversarial Networks with Limited Data Open
Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes train…
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Image Matching from Handcrafted to Deep Features: A Survey Open
As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods ha…
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Balance diagnostics after propensity score matching Open
Propensity score matching (PSM) is a popular method in clinical researches to create a balanced covariate distribution between treated and untreated groups. However, the balance diagnostics are often not appropriately conducted and reporte…
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Optimal Transport for Domain Adaptation Open
Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but…
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Psychological targeting as an effective approach to digital mass persuasion Open
Significance Building on recent advancements in the assessment of psychological traits from digital footprints, this paper demonstrates the effectiveness of psychological mass persuasion—that is, the adaptation of persuasive appeals to the…
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A Deep Relevance Matching Model for Ad-hoc Retrieval Open
In recent years, deep neural networks have led to exciting breakthroughs in\nspeech recognition, computer vision, and natural language processing (NLP)\ntasks. However, there have been few positive results of deep models on ad-hoc\nretriev…
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Data Augmentation Generative Adversarial Networks Open
Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However sta…
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RGB-Infrared Cross-Modality Person Re-identification Open
Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitab…
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Analysis of matched case-control studies Open
There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a “matched analysis” is required. However, mat…
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DeMoN: Depth and Motion Network for Learning Monocular Stereo Open
In this paper we formulate structure from motion as a learning problem. We\ntrain a convolutional network end-to-end to compute depth and camera motion\nfrom successive, unconstrained image pairs. The architecture is composed of\nmultiple …
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Circular RNA identification based on multiple seed matching Open
Computational detection methods have been widely used in studies on the biogenesis and the function of circular RNAs (circRNAs). However, all of the existing tools showed disadvantages on certain aspects of circRNA detection. Here, we prop…
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NewsQA: A Machine Comprehension Dataset Open
We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting o…
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Gated Self-Matching Networks for Reading Comprehension and Question Answering Open
In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurren…
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Bilateral Multi-Perspective Matching for Natural Language Sentences Open
Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In thi…
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The Matching Law: A Research Review Open
Originally published in 1988, the purpose of this title was to present a coherent summary of the previous 30 years’ of research on the way in which animals and humans distribute their behaviour between alternative sources of reinforcement.…
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Statistical primer: propensity score matching and its alternatives† Open
Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling…
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Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners Open
This report aims to provide methodological guidance to help practitioners select the most appropriate weighting method based on propensity scores for their analysis out of many available options (eg, inverse probability treatment weights, …
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End-to-End Neural Ad-hoc Ranking with Kernel Pooling Open
This paper proposes K-NRM, a kernel based neural model for document ranking.\nGiven a query and a set of documents, K-NRM uses a translation matrix that\nmodels word-level similarities via word embeddings, a new kernel-pooling\ntechnique t…