Representation (politics)
View article: Enriching Word Vectors with Subword Information
Enriching Word Vectors with Subword Information Open
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to eac…
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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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BioBERT: a pre-trained biomedical language representation model for biomedical text mining Open
Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature ha…
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Dynamic Graph CNN for Learning on Point Clouds Open
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have lon…
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Using Embeddings to Improve Named Entity Recognition Classification with Graphs Open
Richer information has potential to improve performance of NLP (Natural Language Processing) tasks such as Named Entity Recognition. A linear sequence of words can be enriched with the sentence structure, as well as their syntactic structu…
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LLM-Supported Manufacturing Mapping Generation Open
In large manufacturing companies, such as Bosch, that operate thousands of production lines with each comprising up to dozens of production machines and other equipment, even simple inventory questions such as of location and quantities of…
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Inductive Representation Learning on Large Graphs Open
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in th…
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Representation Learning with Contrastive Predictive Coding Open
While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose…
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Representation Learning with Contrastive Predictive Coding Open
While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose…
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Open
Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understandi…
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PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools Open
PANTHER (Protein Analysis Through Evolutionary Relationships, http://pantherdb.org) is a resource for the evolutionary and functional classification of genes from organisms across the tree of life. We report the improvements we have made t…
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Res2Net: A New Multi-Scale Backbone Architecture Open
Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to con…
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Deep Learning for Generic Object Detection: A Survey Open
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful…
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Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties Open
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either const…
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Hierarchical Text-Conditional Image Generation with CLIP Latents Open
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CL…
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Solving the quantum many-body problem with artificial neural networks Open
Machine learning and quantum physics Elucidating the behavior of quantum interacting systems of many particles remains one of the biggest challenges in physics. Traditional numerical methods often work well, but some of the most interestin…
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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Open
Estimating the 6D pose of known objects is important for robots to interact with the real world.The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects.…
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Neural Discrete Representation Learning. Open
Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Va…
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. Open
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spac…
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Digital Economics Open
Digital technology is the representation of information in bits. This technology has reduced the cost of storage, computation, and transmission of data. Research on digital economics examines whether and how digital technology changes econ…
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Neural Discrete Representation Learning Open
Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Va…
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments Open
Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large nu…
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Deep Contextualized Word Representations Open
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our…
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Grounded theory research: A design framework for novice researchers Open
Background: Grounded theory is a well-known methodology employed in many research studies. Qualitative and quantitative data generation techniques can be used in a grounded theory study. Grounded theory sets out to discover or construct th…
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Classification Based on Decision Tree Algorithm for Machine Learning Open
Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending…
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Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records Open
Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling …
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HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification Open
Hyperspectral image (HSI) classification is widely used for the analysis of\nremotely sensed images. Hyperspectral imagery includes varying bands of images.\nConvolutional Neural Network (CNN) is one of the most frequently used deep\nlearn…
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Analyzing Learned Molecular Representations for Property Prediction Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
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Hypergraph Neural Networks Open
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for com…
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DARTS: Differentiable Architecture Search Open
This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable…