Hash table ≈ Hash table
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Instant neural graphics primitives with a multiresolution hash encoding Open
Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing qualit…
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A Survey on Learning to Hash Open
Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely stu…
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Deep Hashing Network for Efficient Similarity Retrieval Open
Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing, which improves the quality of hash coding by exploiting the sema…
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V0.1IC Open
Hamming Cube, a deep learning framework that jointly learns compact binary codes and continuous embeddings while preserving Hamming distance structure.
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Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval Open
Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying corr…
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Heavy-Hitter Detection Entirely in the Data Plane Open
Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane …
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Differentially Private High-Dimensional Approximate Range Counting, Revisited Open
Locality Sensitive Filters are known for offering a quasi-linear space data structure with rigorous guarantees for the Approximate Near Neighbor search (ANN) problem. Building on Locality Sensitive Filters, we derive a simple data structur…
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#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning Open
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that coun…
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Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval Open
With benefits of low storage cost and fast query speed, cross-modal hashing has received considerable attention recently. However, almost all existing methods on cross-modal hashing cannot obtain powerful hash codes due to directly utilizi…
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Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks Open
This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with ea…
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Column Sampling Based Discrete Supervised Hashing Open
By leveraging semantic (label) information, supervised hashing has demonstrated better accuracy than unsupervised hashing in many real applications. Because the hashing-code learning problem is essentially a discrete optimization problem w…
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Deep Visual-Semantic Hashing for Cross-Modal Retrieval Open
Due to the storage and retrieval effciency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables effcient retrieval of images in response to text qu…
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Asymmetric Deep Supervised Hashing Open
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in m…
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Compacting de Bruijn graphs from sequencing data quickly and in low memory Open
Motivation : As the quantity of data per sequencing experiment increases, the challenges of fragment assembly are becoming increasingly computational. The de Bruijn graph is a widely used data structure in fragment assembly algorithms, use…
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Deep Quantization Network for Efficient Image Retrieval Open
Hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing improves the quality of hash coding by exploiting the semantic similarity on data pairs and has received increa…
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MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval Open
Hashing has recently sparked a great revolution in cross-modal retrieval because of its low storage cost and high query speed. Recent cross-modal hashing methods often learn unified or equal-length hash codes to represent the multi-modal d…
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Graph PCA Hashing for Similarity Search Open
This paper proposes a new hashing framework to conduct similarity search via the following steps: first, employing linear clustering methods to obtain a set of representative data points and a set of landmarks of the big dataset; second, u…
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A Survey on Deep Hashing Methods Open
Nearest neighbor search aims at obtaining the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most w…
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dropClust: efficient clustering of ultra-large scRNA-seq data Open
Droplet based single cell transcriptomics has recently enabled parallel screening of tens of thousands of single cells. Clustering methods that scale for such high dimensional data without compromising accuracy are scarce. We exploit Local…
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Semantic Structure-based Unsupervised Deep Hashing Open
Hashing is becoming increasingly popular for approximate nearest neighbor searching in massive databases due to its storage and search efficiency. Recent supervised hashing methods, which usually construct semantic similarity matrices to g…
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Deep Supervised Discrete Hashing Open
With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefit from recent advances in deep learning, deep hashing methods have achieved promising results f…
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Latent Semantic Minimal Hashing for Image Retrieval Open
Hashing-based similarity search is an important technique for large-scale query-by-example image retrieval system, since it provides fast search with computation and memory efficiency. However, it is a challenge work to design compact code…
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Deep Binary Reconstruction for Cross-Modal Hashing Open
To satisfy the huge storage space and organization capacity requirements in addressing big multimodal data, hashing techniques have been widely employed to learn binary representations in cross-modal retrieval tasks. However, optimizing th…
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Dynamic Multi-View Hashing for Online Image Retrieval Open
Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-base…
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Robust Image Hashing with Tensor Decomposition Open
This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor. Specifically, a stable three-orde…
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Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features Open
Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-cra…
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Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval Open
In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results…
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A Blockchain-Based Decentralized Data Storage and Access Framework for PingER Open
The blockchain is an innovative technology which opened doors to new applications for solving numerous problems in distributed environments. In this study, we design a blockchain-based data storage and access framework for PingER (worldwid…
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Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals Open
Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable imag…
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Online Cross-Modal Hashing for Web Image Retrieval Open
Cross-modal hashing (CMH) is an efficient technique for the fast retrieval of web image data, and it has gained a lot of attentions recently. However, traditional CMH methods usually apply batch learning for generating hash functions and c…