Bayesian SPLDA adapts model parameters for database transfer.
problem Adapting SPLDA for new databases with limited data.
method Variational Bayes estimation of SPLDA parameters.
result Adaptation of SPLDA model parameters for database transfer.
Super Learner improves prediction in electronic healthcare databases.
problem Improving prediction models in large electronic healthcare databases.
method Super Learner ensemble learning algorithm combined with high-dimensional propensity score variable selection.
result Super Learner adapts to different datasets and optimizes predictive performance.
Derives equations for a Variational Bayes i-vector extractor.
problem Reduce overfitting or adapt i-vectors with limited data.
method Joint factor analysis and variational principal components.
result Derives equations for efficient i-vector extraction.
CNN classifies sleep-wake states from heart rate variability.
problem Classifying sleep-wake states from heart rate data.
method Convolutional Neural Network (CNN) trained on ECG data.
result CNN achieves high accuracy in classifying wake/sleep stages.
New method explains color vision's nonlinearities and adaptability.
problem Understanding human color vision's nonlinear and adaptive aspects.
method Sequential Principal Curves Analysis (SPCA) with local metric.
result Color discrimination thresholds and illuminant discount emerge from realistic data.
Paper proposes a method to predict optimal data partitioning based on query execution costs.
problem Finding optimal data partitioning for improved system performance and scalability.
method Formal model abstraction of workload queries, genetic algorithm for optimization, evaluation using PostgreSQL's query optimizer.
result The approach effectively reduces workload execution cost and improves system performance.
Differentially private data structures for estimating distances between strings.
problem Estimating distances between query strings and database strings while ensuring privacy.
method Proposes differentially private data structures for Hamming and edit distances using randomized response technique.
result Efficient data structures that provide accurate distance estimates with strong privacy guarantees.
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.
Images seen during test time are often not from the same distribution as images used for learning. This problem, known as domain shift, occurs when training classifiers from object-centric internet image databases and trying to apply them directly to scene understanding tasks. The consequence is often severe performanc…
ECG-DelNet uses neural networks to accurately delineate ECGs, even with low-quality data.
problem Lack of model explainability and small databases limit deep learning applicability in ECG detection.
method Adapted U-Net architecture for 1D data, used PhysioNet's QT database, applied data augmentation and regularization techniques.
result Best configuration achieved high precision and recall for P, QRS, and T waves.
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.
Database of Irish parliamentary speeches from 1919-2013.
problem Storing and analyzing historical parliamentary debates.
method Downloaded speeches from official website, processed, parsed, and matched with TD and minister data.
result 4.5 million speeches from 1,178 TDs in a single database.
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.
This study applies variational inference to improve music emotion recognition.
problem Improving understanding and recognition of music emotions.
method Employed variational inference and Bayesian statistics techniques.
result Developed a flexible multivariate model for emotion recognition.
Alpha MAML adapts MAML for fewer hyperparameter tuning.
problem Costly hyperparameter tuning in MAML.
method Online hyperparameter adaptation scheme.
result Substantial reduction in hyperparameter tuning needed.
New method selects correlated features for unsupervised domain adaptation.
problem Improving unsupervised domain adaptation performance.
method Optimal transport theory for feature selection.
result Proposes a novel algorithm for feature selection.
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.
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…
This paper presents an assessment of global economic energy potentials for all major natural energy resources. This work is based on both an extensive literature review and calculations using natural resource assessment data. Economic potentials are presented in the form of cost-supply curves, in terms of energy flows …
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.
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.
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.
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.
This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. As co…
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.
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…
Paper proposes a new method for robust speaker verification.
problem Improving robustness in speaker verification systems.
method Combines soft VAD and self-adaptive VAD with DNN-based VAD.
result Significant improvement in verification performance in real-world environments.
Pyrocast predicts pyrocumulonimbus clouds six hours before they form.
problem Predicting pyrocumulonimbus clouds to adapt to climate change.
method Database of 148 pyroCb events, Random Forests, CNNs, and Auto-Encoders.
result Best model predicts pyroCb with 90% accuracy.
In this paper, a modification to the training process of the popular SPLICE algorithm has been proposed for noise robust speech recognition. The modification is based on feature correlations, and enables this stereo-based algorithm to improve the performance in all noise conditions, especially in unseen cases. Further,…
New filters match advanced composition for adaptive privacy, with practical constants.
problem Limitations of existing adaptive composition methods.
method Constructed new filters and odometers that match advanced composition rates, including constants.
result Achieved fully adaptive privacy with practical filters and odometers.
Bayesian algorithm detects image matches and fraud.
problem Detecting identity matches and fraud in image databases.
method Generative model of image graph trained with matching algorithm.
result Bayesian approach improves detection accuracy.
Paper improves text-to-SQL translation by encoding schema relations with self-attention.
problem Improving text-to-SQL translation accuracy across diverse databases.
method Uses relation-aware self-attention to encode schema information.
result Significant gains on Spider dataset (42.94% exact match accuracy).
Graph Neural Networks improve machine learning on relational databases.
problem Training machine learning models on relational databases requires costly data extraction and feature engineering.
method Uses Graph Neural Networks to extract features from relational databases.
result Outperforms state-of-the-art automatic feature engineering methods.
Novel algorithm compresses ECG signals with preserved R peaks.
problem Efficiently compressing ECG signals while preserving R peak information.
method Blaschke unwinding AFD for faster convergence and higher fidelity.
result The proposed algorithm outperforms state-of-the-art approaches in ECG signal compression.
A new ranking algorithm learns data affinity and ranking scores simultaneously.
problem Retrieving similar objects in large databases is challenging.
method Proposes a ranking algorithm that learns data affinity and ranking scores simultaneously, using adaptive neighbors and smoothness constraints.
result The proposed algorithm outperforms existing methods in synthetic and real datasets.
Paper presents a new Wi-Fi RSS and geomagnetic field database for indoor localization and trajectory estimation.
problem Indoor localization and trajectory estimation challenges.
method Convolutional neural network (CNN) for RSS data and LSTM network for geomagnetic field intensity.
result CNN and LSTM networks show feasibility for localization and trajectory estimation.
New deep learning method validated across multiple sleep staging databases.
problem Improving automatic sleep scoring accuracy across different datasets.
method Ensemble of local models using deep learning for automatic sleep staging.
result Good general performance compared to human experts and state-of-the-art methods.
A new algorithm adapts to changing user behaviors in finance.
problem Adapting to changing user behaviors in financial recommendations.
method History-Augmented Collaborative Filtering using a custom neural network.
result The algorithm provides dynamic financial recommendations.
EERN uses deep learning for relational databases, outperforming other methods.
problem Deep learning for relational databases.
method Equivariant Entity-Relationship Network (EERN) using MLP equivariant to Entity-Relationship model symmetries.
result EERN outperforms other methods in synthetic and real-data experiments.
New method protects privacy while allowing accurate statistical inference from synthetic data.
problem Ensuring privacy in database release while maintaining statistical utility.
method Kernel mean embedding with differential privacy constraints.
result Consistent estimators of population statistics can be constructed while protecting individual privacy.
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.
RIO uses rotation-equivariance to train robust inertial odometry models.
problem Training robust inertial odometry models with limited labeled data.
method Rotation-equivariance as self-supervisor, adaptive Test-Time Training (TTT).
result RIO-trained models achieve on-par performance with full-labeled data models.
Transfer learning improves handwriting recognition in small datasets.
problem Reduced training datasets for handwriting text recognition.
method Transfer learning from a larger database to a smaller one, focusing on retraining the whole CNNLSTM-CTC model.
result Significant improvement in character error rate (CER) with transfer learning, reducing CER from 18.2% to 3.3%.
FASE-AL uses active learning to reduce labeling costs for data streams.
problem Reduction of labeling costs for data stream classification.
method Combines Fast Adaptive Stacking of Ensembles (FASE) with active learning.
result Achieves high accuracy with minimal labeled data.
Neural network improves breast cancer diagnosis with high accuracy.
problem Improving accuracy in breast cancer diagnosis.
method Higher-order probabilistic perceptron (HOPP) model.
result HOPP model achieves up to 97% accuracy in classifying breast cancer tumors.
Interactive image retrieval system learns from user feedback and unlabeled data.
problem Efficiently retrieve relevant images with minimal user interaction.
method Combines active learning and graph-based semi-supervised learning (GSSL) to use unlabeled data.
result High F1 scores with few relevance feedback rounds on large datasets.
CNNs improve supernovae classification.
problem Classifying supernovae light-curves efficiently.
method Adapted CNNs for time series, Siamese CNN for sparsity.
result CNNs outperform current methods.
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.