Results 11 to 20 of about 9,428,499 (297)
Multi-Scale Representation Learning on Proteins
CoRR, 2021 Proteins are fundamental biological entities mediating key roles in cellular function and disease. This paper introduces a multi-scale graph construction of a protein -- HoloProt -- connecting surface to structure and sequence. The surface captures coarser details of the protein, while sequence as primary component and structure -- comprising secondary
Somnath, Vignesh Ram +2 more
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Robustifying the Multi-Scale Representation of Neural Radiance Fields [PDF]
CoRR, 2022 Neural Radiance Fields (NeRF) recently emerged as a new paradigm for object representation from multi-view (MV) images. Yet, it cannot handle multi-scale (MS) images and camera pose estimation errors, which generally is the case with multi-view images captured from a day-to-day commodity camera.
Nishant Jain +2 more
core +5 more sources
Conformal-Based Surface Morphing and Multi-Scale Representation [PDF]
Axioms, 2014 This paper presents two algorithms, based on conformal geometry, for the multi-scale representations of geometric shapes and surface morphing. A multi-scale surface representation aims to describe a 3D shape at different levels of geometric detail, which
Ka Chun Lam, Chengfeng Wen, Lok Ming Lui
doaj +4 more sources
Learning Multi-Scale Representations for Material Classification [PDF]
CoRR, 2014 The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors. In computer vision, success stories of learned features have been predominantly reported for object recognition tasks.
Li, W., Fritz, M.
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Multi-Scale Graphical Representation of Cell Environment
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022 We present a multi-scale graphical network that can capture the relevant representations of individual cell morphology, topological structure of cell communities in a tissue image, as well as whole slide level attributes. This helps to effectively merge the disease relevant cell morphology to the overall topological context within the sample, within ...
Theissen, H +4 more
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Multi-Scale Representation of High Frequency Market Liquidity [PDF]
SSRN Electronic Journal, 2014 We introduce an event based framework mapping financial data onto a state based discretisation of time series. The mapping is intrinsically multi-scale and naturally accommodates itself with tick-by-tick data. Within this framework, we define an information theoretic quantity that characterises the unlikeliness of price trajectories and, akin to a ...
Anton Golub +3 more
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Multi-scale contrastive siamese networks for self-supervised graph representation learning [PDF]
, 2021 Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To overcome this problem, inspired by the recent success of graph contrastive
Chen Gong +17 more
core +1 more source
Algorithms for Mobile Robot Localization and Mapping, Incorporating Detailed Noise Modeling and Multi-scale Feature Extraction [PDF]
, 2006 Mobile robot localization and mapping in unknown environments is a fundamental requirement for effective autonomous navigation. Three different approaches to localization and mapping are presented.
Pfister, Samuel Thomas
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Multi-scale Representation of DEM Based on Fractional Contourlet Transform [PDF]
Jisuanji gongcheng, 2018 Most of the existing Digital Elevation Model(DEM) multi-resolution representation method are redundant transformation.The redundancy increases sharply with the increase of the scale of the transformation,so it cannot effectively express the multi-scale ...
WANG Haijiang,YAO Fuqi,LI Lihong,MA Yongqiang,YANG Qinke,WANG Jingpu
doaj +1 more source
Multi-Scale Fusion for Object Representation
CoRRRepresenting images or videos as object-level feature vectors, rather than pixel-level feature maps, facilitates advanced visual tasks. Object-Centric Learning (OCL) primarily achieves this by reconstructing the input under the guidance of Variational Autoencoder (VAE) intermediate representation to drive so-called slots to aggregate as much object ...
Pajarinen Joni +3 more
openaire +6 more sources