Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari…
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48 results for “Large Databases”
CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.
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 many applications. However, most existing deep supervised hashing methods adopt a …
Extends Aff-Wild database for affect recognition in real-world settings.
problem Complex human emotional states in real-world settings.
method Developed deep neural architectures with attention mechanism for emotion recognition.
result Improved performance in emotion recognition using Aff-Wild2.
This work applies SQL to deep learning, leveraging database techniques.
problem Applying deep learning techniques to databases.
method Expressing deep learning operations using SQL, a multidimensional array language.
result Demonstrates the feasibility of using SQL for deep learning operations.
New taxonomy for structured missingness in large-scale databases.
problem Handling missing values in structured data.
method Introducing a new taxonomy for Structured Missingness (SM) and embedding it within existing mechanisms.
result Demonstrated the impact of Structured Missingness on inference and prediction.
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networksq-bio.BM
Deep neural network identifies potential SARS-CoV-2 inhibitors.
problem Finding novel therapies for SARS-CoV-2.
method Used ChemAI, a deep neural network trained on 220M data points, to screen and rank one billion molecules from the ZINC database.
result Identified 30,000 top-ranked compounds for further bioassays.
Graph database outperforms in filtering ESG stocks efficiently.
problem Efficiently filtering ESG stocks from large lists of equities.
method Compared SQL, No-SQL, and graph databases; used Python for database interactions; collected data from stock price and financial news.
result Graph database is more efficient for ESG stock filtering.
New vector quantization method reduces relevance of parallel components in database points.
problem Scaling maximum inner product search to massive databases.
method Developed anisotropic vector quantization loss functions.
result Achieves state-of-the-art results on public benchmarks.
TURB-Rot provides a large database of turbulent rotating flow snapshots for research.
problem Lack of large-scale, high-resolution datasets for turbulent rotating flows.
method Direct Numerical Simulations of Navier-Stokes equations with rotation.
result Provides a diverse set of 300K complex images and fields for testing.
A new SVM method using bagging and importance sampling.
problem Solving SVM problems efficiently in large databases.
method Bagging and importance sampling applied to SVM.
result Faster solution of SVM problem with minimal loss in prediction error.
Federated learning enables secure meta-analysis of large medical brain datasets.
problem Privacy and legal concerns prevent direct sharing of brain imaging data across different institutions.
method Developed a federated learning framework to securely access and analyze brain data from multiple databases.
result The framework successfully analyzed brain structural relationships across various diseases and cohorts.
PyODDS automates outlier detection for new data sources.
problem Manual outlier detection is inefficient and domain-specific.
method Automated end-to-end system with Apache Spark and database support.
result PyODDS optimizes outlier detection pipelines automatically.
Machine learning predicts band gaps for large organic crystals.
problem Predicting band gaps for complex organic crystal structures.
method Released a dataset of 12,500 crystal structures and their band gaps. Trained two state-of-the-art models to achieve a mean absolute error of 0.388 eV.
result Trained models predict band gaps with 13% error for an average gap of 3.05 eV.
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressionscs.CV
Paper tackles multi-task learning for emotion recognition and generation using Aff-Wild dataset.
problem Developing a multi-task learning approach for emotion recognition and generation using Aff-Wild dataset.
method Deep neural network with shared hidden layers and GAN for multi-task learning and image generation.
result Good performance of the proposed approach on Aff-Wild dataset.
In this paper we deal with the offline handwriting text recognition (HTR) problem with reduced training datasets. Recent HTR solutions based on artificial neural networks exhibit remarkable solutions in referenced databases. These deep learning neural networks are composed of both convolutional (CNN) and long short-ter…
The paper studies causal effects of multiple treatments in healthcare databases with rare outcomes.
problem Estimating causal effects of multiple treatments in healthcare databases with rare outcomes.
method The paper designs three sets of simulations and compares the operating characteristics of three types of methods: Bayesian Additive Regression Trees (BART), regression adjustment on multivariate spline of generalized propensity scores (RAMS), and inverse probability of treatment weighting (IPTW) with multinomial logistic regression or generalized boosted models.
result BART and RAMS provide lower bias and mean squared error compared to IPTW methods.
Deep transfer learning improves sleep staging with small datasets.
problem Insufficient data for deep neural networks due to channel mismatch.
method Transfer learning from large to small datasets, finetuning the pretrained network.
result Significant performance improvement in sleep staging.
Deep learning features improve CBIR system performance.
problem Retrieving similar images from a large database.
method Using features from pre-trained deep learning models for similarity retrieval.
result Significantly superior retrieval results compared to traditional methods.
This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the len…
Distance Correlation Methods for Discovering Associations in Large Astrophysical Databasesastro-ph.CO
High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…
New method embeds DNA sequences for faster, more informative gene comparison.
problem Slow and costly sequence comparison methods for genes without exact matches.
method Recurrent neural networks to embed sequences in a low-dimensional space.
result Embedding allows for better comparison of genes without exact matches.
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a "library" of candidate prediction models. The SL is not restricted to a single prediction model, but uses …
With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening. Most post-marketing detections of adverse drug reaction (ADR) rely on physicians' spontaneous reports, leading to under reporting. To take up this challenge, we develop a scalable…
Aligns databases with Gaussian features using MAP estimation and thresholding.
problem Aligning databases with jointly Gaussian features.
method Two algorithms: MAP estimation and thresholding of log likelihood ratios.
result Conditions on mutual information for reliable performance of algorithms.
Study evaluates uncertainty quantification for atomistic neural networks, revealing complex relationships between error and uncertainty.
problem Uncertainty quantification for predictions of atomistic neural networks.
method Modified PhysNet NN architecture, evaluated with various metrics, analyzed QM9 and tautomerization reaction databases.
result Error and uncertainty are not linearly related; redundancy and noise complicate predictions, especially for small changes.
Neural model uses deductive database to predict events from past patterns.
problem Difficulty in predicting future events from past patterns when event types are large.
method Temporal deductive database with rules to prove facts from other facts and past events. Neural nets model fact states and probabilities.
result Neural models derived from concise Datalog programs improve prediction by encoding domain knowledge.
Study tackles database variability in medical data using ensemble models and CNNs.
problem Achieving robust generalization in machine learning models across multiple medical databases.
method Ensemble of local models based on convolutional neural networks (CNNs) and various data preprocessing techniques.
result Improved inter-database generalization performance and scalability of models.
Graph-based framework predicts ADR signals from clinical data.
problem Detecting ADRs in post-market surveillance using clinical data.
method Developed a Drug-disease graph with Graph Neural Network for ADR signal prediction.
result Improved AUROC and AUPRC performance (0.795 and 0.775) compared to other algorithms.
Deep transfer learning improves automatic sleep staging accuracy.
problem Small cohort data variability and inefficiency in sleep studies.
method Deep transfer learning approach using a large dataset to a small cohort.
result Significant performance improvement on automatic sleep staging.
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases. In …
New STH distance finds patterns in event timeseries without resampling.
problem Lack of efficient analysis methods for event and state timeseries.
method Define STE-ts, propose STH, leveraging both time and state duration.
result Improved precision and computation time compared to resampled metrics.
Study connects database alignment and planted matching using Gaussian features.
problem Identify matching between correlated user features in anonymized databases.
method Derived results for database alignment and planted matching, showing connections and thresholds.
result Performance thresholds for database alignment converge to planted matching when feature dimensionality is sufficiently high.
Database theory and database practice are typically the domain of computer scientists who adopt what may be termed an algorithmic perspective on their data. This perspective is very different than the more statistical perspective adopted by statisticians, scientific computers, machine learners, and other who work on wh…
Study speaker verification security using hierarchical Bayesian modeling.
problem Estimating false alarm rate in ASV systems for large speaker databases.
method Hierarchical Bayesian modeling of ASV scores to assess security against closest impostors.
result Neither i-vector nor x-vector systems are secure against increased impostor database size.
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event datastat.ML
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditio…
Eigenrank selects images for deep learning training and predicts segmentation failures.
problem Challenges in automating deep learning for medical image segmentation.
method Eigenrank selects images for training and predicts failures using Von Neumann information.
result U-Net trained on Eigenrank-selected images outperforms one trained on random images.
This paper predicts registration error in medical images using a random regression forest.
problem Predicting registration error in medical images without a ground truth.
method Random regression forest trained on features related to transformation model and dissimilarity after registration.
result The method achieves good performance in automatic quality control of large-scale image analysis.
A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the …
Study compares CDS databases and finds discrepancies due to various factors.
problem Comparing discrepancies among CDS databases.
method Comparing five major sources of corporate CDS prices over 2004-2010.
result CMA quotes lead price discovery and databases disagree on stock-CDS return analysis.
First ABAW 2020 Competition analyzes affective behavior tasks.
problem Automatic analysis of valence-arousal, basic expressions, and action units in real-world scenarios.
method Provided Aff-Wild2 database, described Challenges, evaluation metrics, and top-performing systems.
result Demonstrated the feasibility of automatic affective behavior analysis in real-world settings.
PyODDS is a Python system for outlier detection in databases.
problem Outlier detection in databases for various users.
method End-to-end Python system with database support, including statistical and deep learning algorithms.
result Supports outlier detection algorithms for users with or without machine learning background.
The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge …
Paper presents a fast framework for root cause analysis in large-scale systems.
problem Challenges in reviewing logs for identifying issues in large-scale production environments.
method Automates root cause analysis on structured logs with improved scalability using frequent item-set mining and association rule learning.
result Proposes a framework that selects unique item-sets for target failures, improving interpretability and scalability.
Taxonomies of cryptocurrencies and comparisons with fiat money and databases.
problem Comparing cryptocurrencies, fiat money, and databases.
method Taxonomy creation and comparison analysis.
result Cryptocurrencies and databases have unique characteristics not found in fiat money.
An interactive image retrieval system learns which images in the database belong to a user's query concept, by analyzing the example images and feedback provided by the user. The challenge is to retrieve the relevant images with minimal user interaction. In this work, we propose to solve this problem by posing it as a …
Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values. Bayesian methods allow flexible generative models that share power across databases as well as principled quantification of uncertain…
We present a database of parliamentary debates that contains the complete record of parliamentary speeches from Dáil Éireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta Dála, members of parliament), such…