A new hashing method handles complex multi-level labels.
problem Handling complex multi-level labels in cross-modal data retrieval.
method Derives a semantic ranking list from feature and label information, integrates semantic ranking into deep cross-modal hashing.
result RDCMH outperforms other methods in cross-modal retrieval applications.
Efficient Discrete Supervised Hashing improves cross-modal retrieval by preserving semantic correlations and reducing quantization error.
problem Challenges in preserving semantic correlations and reducing quantization error in cross-modal hashing for large-scale retrieval.
method Collective matrix factorization on heterogenous features and semantic embedding with class labels to learn hash codes efficiently.
result EDSH produces superior performance in both accuracy and scalability over existing methods.
New hashing method improves document retrieval precision.
problem Efficiently retrieving similar documents from large text databases.
method Pairwise supervised hashing with Bernoulli VAE and unbiased gradient estimator.
result Superior performance compared to existing methods.
Enhances hashing for fast retrieval with correlated bits.
problem Fast retrieval and small memory footprint for large-scale information retrieval.
method Employing Boltzmann machine distribution as variational posterior to model correlations among hash code bits.
result Significant performance gains achieved by effectively modeling correlations among hash code bits.
ForestHash combines random forests and CNNs for efficient data hashing.
problem Efficiently hashing large datasets while preserving similarity.
method Random forests with light-weight CNNs, grouping classes, and information-theoretic aggregation.
result Significantly outperforms state-of-the-art hashing methods for image retrieval.
This paper improves search efficiency by augmenting autoencoder encodings.
problem Improving search speed and relevance in information retrieval.
method Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing.
result Gradient Augmented Search (GSA) enhances TF-IDF-based systems.
Hash codes are a very efficient data representation needed to be able to cope with the ever growing amounts of data. We introduce a random forest semantic hashing scheme with information-theoretic code aggregation, showing for the first time how random forest, a technique that together with deep learning have shown spe…
A new hashing framework learns multiple hash codes for each image to improve hash bucket search efficiency.
problem Existing hashing methods fail to handle complex image retrieval scenarios efficiently.
method Multiple Code Hashing (MCH) framework with deep reinforcement learning.
result Significant improvement in hash bucket search performance compared to single-code methods.
Paper introduces EM-KSH and EM-SPLH methods for supervised hashing.
problem Efficiently optimize retrieval speed and storage cost while preserving semantic information.
method Convert supervised hashing formulations to CRF, solve consistency equations using linear approximation of sigmoid function.
result Experimental results show superior performance of EM-KSH and EM-SPLH.
Autoencoders improve sequence model representations.
problem Challenges in extracting good representations from recurrent models.
method Integrates autoencoder with sequence models to compress through discrete latent space.
result Improved latent codes correspond to words and phrases, enhancing model performance.
Binary embeddings speed up graph data retrieval.
problem Efficiently retrieving graphical data.
method Binary valued embeddings modeled as coin flips with varying bias, optimized using continuous optimization techniques.
result Binary embeddings outperform other methods on various datasets.
Self-supervised learning of visual semantics in image games.
problem Learning visual semantics in referential emergent language games.
method Investigating the impact of feature extractor weights and tasks on visual semantics, using various image augmentations and additional tasks.
result Communication systems can learn visual semantics in a self-supervised manner by playing the right types of games.
This paper investigates semi-supervised hashing methods using variational autoencoders.
problem Semantic hashing with scarce labels.
method Two semi-supervised approaches: joint modeling and pairwise loss.
result The pairwise approach can improve hash quality with many labeled points but degrades with few labels.
We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semanti…
In this paper, a simple, general method of adding auxiliary stochastic neurons to a multi-layer perceptron is proposed. It is shown that the proposed method is a generalization of recently successful methods of dropout (Hinton et al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and semantic has…
apk2vec builds profiles of Android apps using multiple views, outperforming existing methods.
problem Building comprehensive behavior profiles of Android apps for better analytics.
method Semi-supervised multi-view representation learning combining RL and feature hashing.
result apk2vec's profiles significantly outperform state-of-the-art techniques in app analytics tasks.
ADSH uses asymmetric deep hashing for faster training and better performance.
problem Efficiently utilizing supervised information for large-scale database in deep supervised hashing.
method ADSH learns a deep hash function only for query points and directly learns hash codes for database points.
result ADSH achieves state-of-the-art performance in real applications.
New kernel model for natural language processing improves expressibility and scalability.
problem Limited expressibility and scalability of traditional convolution kernels in natural language processing.
method Proposes a nonstationary kernel model and a stochastic sampling algorithm for scalable learning.
result Demonstrates improved performance on extracting biological models from scientific text.
A new hashing method improves accuracy by learning an orthogonal transform.
problem Improving hashing accuracy for similarity search.
method Procrustean Orthogonal Sparse Hashing (POSH) method.
result POSH outperforms state-of-the-art hashing methods.
The paper compares different hashing schemes for their performance on structured data.
problem The performance of hashing schemes on structured input is not well understood.
method The paper compares mixed tabulation hashing, multiply-mod-prime hashing, and MurmurHash3.
result Mixed tabulation hashing performs similarly to truly random hashing but is faster and has a proven guarantee.
FSDH is a fast supervised hashing method that outperforms existing methods.
problem Efficiently hashing large datasets for fast retrieval.
method FSDH uses a simple regression of class labels to hash codes, avoiding iterative discrete optimization.
result FSDH is significantly faster (12-151x) than existing methods like SDH and FastHash.
FlexCMH learns effective hashing codes from weakly-paired data.
problem Cross-modal hashing assumes perfect correspondence between samples, which is unrealistic.
method FlexCMH uses clustering-based matching to find potential correspondence and jointly optimizes it with hashing functions.
result FlexCMH achieves significantly better results than state-of-the-art methods.
We generated a dataset of 200 GB with 10^9 features, to test our recent b-bit minwise hashing algorithms for training very large-scale logistic regression and SVM. The results confirm our prior work that, compared with the VW hashing algorithm (which has the same variance as random projections), b-bit minwise hashing i…
Generative approach learns hash functions for efficient binary search.
problem Challenges in learning discrete hash functions for fast search.
method Generative approach using Minimum Description Length principle and stochastic distributional gradient.
result Significantly better retrieval results than existing methods.
Improves two-stage hashing methods for better image retrieval.
problem Developing efficient binary codes for image retrieval.
method Theoretical analysis and empirical improvements of two-stage hashing methods using high-capacity hash functions.
result Proposes a novel two-stage hashing method significantly outperforming previous studies.
DSHL generates music using segment concatenation and hash learning.
problem Music generation using deep learning.
method Deep recurrent neural network and ranking-based hash learning.
result DSHL generates original and enjoyable music.
Fuzzy hashes learn from data to improve file similarity detection.
problem Measuring similarity between files, especially malware.
method Learned fuzzy hashes using a minimax training framework.
result Learned fuzzy hashes outperform traditional methods for file similarity.
BioHash improves similarity search performance using sparse high-dimensional hash codes.
problem Improving similarity search performance in high-dimensional data.
method BioHash produces sparse high-dimensional hash codes through a data-driven approach based on synaptic plasticity.
result BioHash outperforms previous hashing methods in various similarity search tasks.
Minwise hashing is the standard technique in the context of search and databases for efficiently estimating set (e.g., high-dimensional 0/1 vector) similarities. Recently, b-bit minwise hashing was proposed which significantly improves upon the original minwise hashing in practice by storing only the lowest b bits of e…
Hyperplane hashing aims at rapidly searching nearest points to a hyperplane, and has shown practical impact in scaling up active learning with SVMs. Unfortunately, the existing randomized methods need long hash codes to achieve reasonable search accuracy and thus suffer from reduced search speed and large memory overhe…
New hashing methods improve entity resolution in conflict datasets.
problem Improving entity resolution in databases with unknown unique identifiers.
method Introducing and comparing new locality sensitive hashing methods.
result New hashing methods outperform existing approaches in conflict datasets.
Fruit fly brain network learns word embeddings using sparse binary codes.
problem Learning semantic word representations from text.
method Inspired by mushroom body neural network, sparse binary hash codes.
result Fruit fly network achieves comparable NLP performance with reduced resources.
The paper develops methods to optimize ranking metrics for hashing.
problem Improving hashing for better retrieval performance.
method Developed tie-aware learning to rank formulations for hashing.
result Established new state-of-the-art for image retrieval by Hamming ranking.
In supervised binary hashing, one wants to learn a function that maps a high-dimensional feature vector to a vector of binary codes, for application to fast image retrieval. This typically results in a difficult optimization problem, nonconvex and nonsmooth, because of the discrete variables involved. Much work has sim…
Enhances hashing for cross-modal retrieval using multi-view features.
problem Limited improvement in single-view hashing for cross-modal retrieval.
method Exploits multiple views to enrich feature information, learning discriminative hash codes.
result Superior performance compared to state-of-the-art methods on various datasets.
SDHR improves data hashing for better classification accuracy.
problem Efficient data hashing for high-dimensional data retrieval.
method Supervised Discrete Hashing with Relaxation (SDHR) using optimized regression targets.
result SDHR outperforms traditional methods in classification accuracy.
A new hashing method handles large-scale data with flexible similarity measures.
problem Efficient nearest neighbour search in large-scale systems with variable labels.
method End-to-end trainable network transforming data to uniform distribution on product of spheres, then hashing to binary form maximizing entropy.
result Outperforms baseline approaches in limited capacity regime.
A novel hash learning approach using codewords in Hamming space.
problem Hash learning for supervised, unsupervised, and semi-supervised scenarios.
method Uses codewords inferred from data to capture grouping aspects of hash codes, with regularization for automatic codeword selection. Solves via Block Coordinate Descent and SVM.
result Demonstrates superior performance in content-based image retrieval.
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approac…
New neural network outperforms existing methods in scene matching.
problem Automated scene matching with high accuracy and low false positives.
method Convolutional hashing using a new loss function and training scheme.
result Significantly higher true positive rate and 100-fold reduction in false positives.
In this paper, we propose to (seamlessly) integrate b-bit minwise hashing with linear SVM to substantially improve the training (and testing) efficiency using much smaller memory, with essentially no loss of accuracy. Theoretically, we prove that the resemblance matrix, the minwise hashing matrix, and the b-bit minwise…
FROSH and DFROSH speed up online sketching hashing for big data.
problem Efficiency and scalability of hashing methods for streaming data.
method Online sketching hashing (OSH) and FasteR Online Sketching Hashing (FROSH) algorithm.
result FROSH reduces training time and maintains sketching precision.
Hashing detects anomalies in structured data efficiently.
problem Identifying non-conforming samples on low-dimensional manifolds.
method Locality Sensitive Hashing in Preference Space.
result State-of-the-art performance at lower computational cost.
Paper reduces recommender system model size by 90%.
problem Large model size in recommender systems.
method Hybrid frequency and double hashing for model size reduction.
result 90% reduction in model size with no performance loss.
New LSH algorithms improve nearest-neighbor search performance.
problem Efficiently searching for similar high-dimensional data.
method High-dimensional locality-sensitive hashing (LSH) based on fruit fly olfactory circuit.
result New LSH algorithms outperform existing methods on benchmark datasets.
A new DP algorithm improves privacy in hashing and sampling for search and learning.
problem Improving privacy in hashing and sampling for large-scale applications.
method Combines differential privacy with one permutation hashing and bin-wise consistent weighted sampling.
result Proposes DP-OPH and DP-BCWS algorithms that enhance privacy while maintaining utility.
We present the first provably sublinear time algorithm for approximate \emph{Maximum Inner Product Search} (MIPS). Our proposal is also the first hashing algorithm for searching with (un-normalized) inner product as the underlying similarity measure. Finding hashing schemes for MIPS was considered hard. We formally sho…
Probabilistic hash embeddings improve online learning of categorical features.
problem Online learning of categorical features with changing vocabulary.
method Probabilistic hash embedding (PHE) with Bayesian online learning.
result PHE mitigates forgetting and maintains high performance in online settings.