New algorithm detects rare categories with few labels.
problem Detecting rare categories with limited labeled data.
method Dimension-driven statistics and kappa-profile.
result Algorithm performs well on separable and non-separable classes.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “rare category detection”
Paper uses GANs for efficient rare disease detection.
problem Efficient detection of rare diseases with limited labeled data.
method Semi-supervised learning with GANs.
result Best precision-recall scores compared to baseline techniques.
Deep learning boosts rare disease detection from medical claims.
problem Improving diagnosis and treatment of rare diseases.
method Generative adversarial networks (GANs) and recurrent neural networks for sequence modeling.
result Accurate prediction with 0.56 PR-AUC, outperforming benchmarks.
New approach detects racial segregation patterns in machine learning systems.
problem Challenges of fairness in machine learning systems due to racial identity.
method Unsupervised learning to detect patterns of segregation.
result Mitigates root cause of social disparities without reifying race.
Detects and mitigates rare subclasses in deep neural networks.
problem Underrepresented classes in training datasets reduce classifier performance.
method Commonality metric for detection, methods for reducing impact during training and exploitation.
result Models compensate for rare subclasses, improving performance and runtime identification.
PRESTO improves rare event prediction by shrinking towards proportional odds model.
problem Difficult to predict rare events due to class imbalance.
method PRESTO relaxes proportional odds model by estimating separate weights for transitions between categories, imposing L1 penalty to shrink towards proportional odds.
result PRESTO consistently estimates decision boundary weights under sparsity assumption, improving rare probability estimation.
Paper develops an algorithm with PAC guarantees for detecting alien categories.
problem Detecting alien categories not seen in training data reliably.
method Develops an algorithm with PAC-style guarantees for alien detection under known upper bounds on alien fraction.
result Empirical results show the algorithm's effectiveness in detecting aliens.
TRAKNN: Efficient Trajectory Aware Spatiotemporal kNN for Rare Meteorological Trajectory Detectionstat.ML
TRAKNN detects rare atmospheric trajectories efficiently.
problem Detecting rare atmospheric anomalies over long periods.
method Unsupervised, recurrence-based kNN algorithm.
result Rare trajectories correspond to physical anomalies.
New method detects novel node categories in graphs with distribution shifts.
problem Detecting novel node categories in graphs with distribution shifts.
method Recall-Constrained Optimization with Selective Link Prediction (RECO-SLIP).
result RECO-SLIP outperforms existing methods in detecting novel node categories.
Simulates patient pathways to detect delayed rare disease diagnoses.
problem Delayed rare disease diagnoses in France, causing health system and patient harm.
method Probabilistic modelling of patient pathways to create an alert system.
result Alert system detects and refers wandering patients to CRMRs.
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturingstat.ML
This paper evaluates data enrichment techniques for rare event detection in manufacturing.
problem Rare events in manufacturing lead to unplanned downtime and high energy consumption.
method Time series data augmentation, sampling, and imputation techniques combined with supervised machine learning.
result Data enrichment enhances rare failure event detection and prediction by up to 48%.
New deep learning method handles rare and imbalanced events in time series.
problem Challenges in event detection in time series data, especially rare and imbalanced events.
method Supervised regression-based deep learning approach that handles various types of events.
result Superior performance across diverse domains, particularly for rare events and imbalanced datasets.
Reduces false positives in classifying rare online platforms.
problem Challenges in accurately identifying rare online platforms with ML.
method Calibrated probabilities and ensembles to reduce bias.
result Significantly reduces false positives in rare event detection.
Detection of rare variants by resequencing is important for the identification of individuals carrying disease variants. Rapid sequencing by new technologies enables low-cost resequencing of target regions, although it is still prohibitive to test more than a few individuals. In order to improve cost trade-offs, it has…
MIM-based GAN improves rare event generation in GANs.
problem Improving rare event generation in GANs.
method Adopting MIM (exponential form of information metric) to replace KL divergence in GANs.
result MIM-based GAN achieves state-of-the-art performance in anomaly detection.
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Soundseess.AS
Batch uniformization improves anomaly detection in sound data.
problem Anomaly scores for rare and frequent normal sounds are not uniform.
method Propose batch uniformization to minimize anomaly scores by weighting samples based on their density.
result Improves performance of unsupervised anomaly detection in sound data.
Since their emergence in the 1990's, the support vector machine and the AdaBoost algorithm have spawned a wave of research in statistical machine learning. Much of this new research falls into one of two broad categories: kernel methods and ensemble methods. In this expository article, I discuss the main ideas behind t…
New algorithm detects outliers from rare abnormal data.
problem Detecting outliers from rare abnormal data in transfer learning.
method Meta-algorithm for transfer learning in outlier detection.
result Meta-algorithm yields strong guarantees and outperforms existing methods.
Paper detects duality obstruction in smooth calibrations.
problem Detecting duality obstruction in smooth calibrations.
method Examine Lawlor cones and calibrations, use gluing results.
result Existence of Lawlor cones without smooth calibrations.
A new method combines OCSVM with representation learning for UAD.
problem Detect anomalies without labeled data, especially in rare or unavailable cases.
method Custom loss formulation that aligns latent features with OCSVM decision boundary.
result Succeeds in detecting small, non-hyperintense lesions in MRI.
In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it posses…
The detection of rare variants is important for understanding the genetic heterogeneity in mixed samples. Recently, next-generation sequencing (NGS) technologies have enabled the identification of single nucleotide variants (SNVs) in mixed samples with high resolution. Yet, the noise inherent in the biological processe…
Improved MCMC for rare events in hidden Markov models.
problem Slow inference and prediction for rare latent states in hidden Markov models.
method Targeted sub-sampling (TASS) over-samples rare latent states, reducing variance in gradient estimation.
result Substantial gains in predictive and inferential accuracy on real and synthetic examples.
Survey of deep learning methods for anomaly detection.
problem Detecting anomalies in various applications.
method Review of deep learning techniques for anomaly detection.
result Comprehensive overview of deep learning methods for anomaly detection.
This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.
problem Rare spurious correlations in neural networks and their privacy risks.
method Introducing spurious patterns correlated with a fixed class to a few training examples, analyzing regularization and Gaussian noise.
result Rare spurious correlations can significantly impact neural network accuracy and privacy, and specific mitigation methods can be effective.
Proposes a method to ensure accurate estimation of rare events in AI systems.
problem Lack of efficiency guarantees in black-box systems for rare-event simulation.
method Integrates deep learning with importance sampling to create a statistically guaranteed estimator.
result Demonstrates effective estimation of rare-event probabilities in AI systems.
Deep learning improves anomaly detection across various fields.
problem Detecting anomalies in data with advanced approaches.
method Survey of deep learning methods for anomaly detection.
result Advancements in deep anomaly detection address unique challenges.
Deep CNNs struggle with rare taxa; one-class classifiers help identify them.
problem Efficiently identifying rare benthic macroinvertebrates in insect monitoring.
method Combining deep CNNs with one-class classifiers to identify rare taxa.
result The proposed approach can improve rare species identification, supporting automation.
Paper proposes active learning for sound event detection with reduced annotation effort.
problem Reducing annotation effort for sound event detection.
method Change point detection for candidate selection, mismatch-first farthest-traversal for selection, training with context recordings.
result The proposed system achieves similar performance to full annotation with only 2% of data, reducing annotation effort.
The Familiarity Hypothesis explains deep open set methods' success in detecting novel objects.
problem Detecting novel objects in open set recognition problems.
method Logits-based detection of absence of familiar features.
result Familiarity-based detection fails in scenarios with both novel and familiar objects.
New invariants detect exotic smooth structures in 4-manifolds.
problem Detect exotic smooth structures in 4-manifolds using invariants of 2-handlebodies.
method Investigates invariants of 4-dimensional 2-handlebodies from the Temperley-Lieb category in positive characteristic.
result Height invariant vanishes on , , and for .
Functor connects 4D 2-handlebodies to ribbon categories, detecting non-deformation diffeomorphisms.
problem Detecting non-deformation diffeomorphisms in 4D 2-handlebodies.
method Constructs a braided monoidal functor from 4D 2-handlebodies to unimodular ribbon categories.
result Functor detects non-deformation diffeomorphisms when is not semisimple and is not factorizable.
Study compares unsupervised and weakly-supervised methods for anomaly detection at the LHC.
problem Detecting new physics signals at the LHC with model-agnostic techniques.
method Compared unsupervised autoencoder (AE) and weakly-supervised Classification Without Labels (CWoLa) methods.
result Both methods complement each other, providing sensitivity to different types of signals.
WEST uses EHRs and expert cases to improve rare disease phenotyping.
problem Limited labeled data for rare diseases.
method Weakly supervised transformer model trained on probabilistic silver-standard labels.
result WEST outperforms existing methods in phenotype classification and subphenotyping.
Deep-PrAE improves rare-event simulation for black-box systems.
problem Evaluating rare safety-critical events in learning-based systems.
method Combines deep neural networks with IS to create statistically guaranteed estimations.
result Deep-PrAE provides accurate bounds on safety-critical event probabilities.
The paper improves consumer preference modeling by considering multiple product categories.
problem Estimating consumer preferences across multiple product categories with varying attributes and price sensitivity.
method Extends matrix factorization techniques to account for time-varying product attributes and out-of-stock products, pooling information across categories to estimate heterogeneity in preferences.
result The model improves over traditional approaches, accurately estimating consumer preferences and price sensitivity.
A new method detects epileptic events in EEG signals by integrating labeler categories.
problem Human oversight of brief epileptic events in EEG signals leads to inaccurate diagnoses.
method Integrates EEG signal features with one-hot encoded labeler categories for improved detection.
result The method outperforms consensus-trained detectors and maintains confidence bounds.
Dataset for object detection at Oktoberfest beer tent.
problem Realistic object detection in a busy, diverse setting.
method Hand-annotated 1,110 images, provided trained models.
result Challenging dataset for object detection.
ECOD detects outliers without parameters, fast and simple.
problem Detecting outliers in large, high-dimensional datasets efficiently and interpretably.
method ECOD estimates empirical cumulative distribution functions per dimension, then computes tail probabilities and outlier scores.
result ECOD outperforms state-of-the-art methods in accuracy, efficiency, and scalability.
Robotic clothing manipulation improved with fashion image analysis techniques.
problem Automated identification of clothing categories and landmarks for robotic tasks.
method Training data augmentation methods and rotation invariant convolutions.
result Our approach outperforms state-of-the-art models on unseen datasets.
Approach for modeling EHR data with rare features, improving prediction and interpretation.
problem Challenges in modeling rare binary features in EHR data.
method Tree-guided feature selection and logic aggregation for large-scale regression.
result Improved prediction and model interpretation of suicide risk in EHR data.
Study categorizes and analyzes emotions in sexist tweets.
problem Lack of defined categories for sexism in NLP.
method Used a new dataset from SemEval-2018 to classify and analyze emotions in sexist tweets.
result Demonstrated the mental state and affectual state of users who tweet in different categories of sexism.
Survey examines anomaly detection methods for deep learning.
problem Out-of-distribution and adversarial examples in deep learning.
method Taxonomy of existing anomaly detection techniques.
result Discussion of strengths and weaknesses of techniques.
Diffusion in a linear potential in the presence of position-dependent killing is used to mimic a default process. Different assumptions regarding transport coefficients, initial conditions, and elasticity of the killing measure lead to diverse models of bankruptcy. One "stylized fact" is fundamental for our considerati…
LSVM with EBT reduces quasar detection errors by 10x.
problem Rare quasar detection in astronomy with high cost of misclassification.
method Linear Support Vector Machine (LSVM) with Ensemble Bagged Trees (EBT) and Learning from Mistakes.
result 10x reduction in False Negative Rate for quasar detection.
A novel resampling technique addresses class imbalance in imbalanced datasets.
problem Class imbalance in real-world datasets, especially in rare event detection.
method Developed two oversampling algorithms: G1Nos 1-Nearest Neighbour.
result Our oversampling algorithms outperform state-of-the-art methods in all metrics.
WSGN detects actions from weak supervision, improving performance on THUMOS14 and Charades.
problem Challenging action detection requires detailed manual supervision.
method WSGN learns action detection from video-level labels, exploiting both video-specific and dataset-wide statistics.
result WSGN achieves significant gains in action detection for THUMOS14 and Charades datasets.
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approachcs.CR
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
problem Data scarcity hinders IoT intrusion detection accuracy.
method Geometric Graph Alignment (GGA) approach to transfer knowledge between network intrusion detection and IoT intrusion detection domains.
result GGA approach boosts IoT intrusion detection performance on multiple datasets.