Global constraints improve cognates detection performance.
problem Improving cognates detection accuracy.
method Rescoring of score matrices using global constraints.
result Significant performance improvements across various datasets.
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.
169,341 papers · 148 categories
Trend · papers per month
48 results for “cognates detection”
Paper revisits Deep Variational Information Bottleneck and proposes a new optimization approach.
problem Limitations of Deep Variational Information Bottleneck in optimizing mutual information.
method Proposes a new optimization approach by circumventing the limitation of requiring both Markov chains during optimisation.
result Shows how to optimise a lower bound for mutual information, circumventing the limitation of requiring both Markov chains.
New link detection results using closures of 3-braids.
problem Link detection using homology theories.
method Closure operations on 3-braids and homology theories.
result Detection of specific links using link Floer homology, Khovanov homology, and annular Khovanov homology.
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.
Flexible pipeline for 3D vehicle detection from 2D images.
problem Current methods lack 3D perception of vehicles and other objects.
method Adopt any 2D detection network, fuse with 3D point cloud, develop model fitting algorithm, refine with CNN.
result 3D detection results rank second among algorithms, demonstrating competencies.
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approachcs.LG
Deep learning improves combustor anomaly detection in gas turbines.
problem Improving anomaly detection performance in gas turbine combustors.
method Hierarchically learned features from exhaust gas temperature sensor measurements using deep learning.
result Deep learning-based anomaly detection significantly improved combustor anomaly detection performance.
Paper proposes RL for real-time smart grid cyber attack detection.
problem Real-time detection of cyber-attacks in smart grids.
method Formulated as POMDP, uses model-free reinforcement learning.
result Effective in timely and accurate detection of cyber-attacks.
Graph energy helps detect communities in networks better than traditional methods.
problem Detecting communities in sparse networks where traditional methods fail.
method Using graph energy based on the full spectrum of adjacency matrices.
result The difference in graph energy between a planted partition model and an Erdős--Rényi network has a distinct transition at the detectability threshold.
New method detects change points in multi-dimensional sequences, controlling false detection.
problem Detecting change points in sequences with multiple dimensions.
method Two-stage approach: select relevant dimensions and CPs, using selective inference.
result Exact inference possible for a class of CP detection methods.
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
problem Detecting PMH signals without prior CSI is challenging and computationally expensive.
method Proposes HEM-ML and HEM-KD schemes using EM and KD-tree for efficient detection.
result Achieves comparable detection results to optimal ML detector with reduced complexity.
Develops slope detection for 3-manifolds with torus boundaries.
problem Determining slopes on the boundary of 3-manifolds with torus boundaries.
method Introduces order-detection and representation-detection of slopes, proving their equivalence.
result Shows how slopes' behavior changes with cabling, improving previous results.
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signaturescs.CV
2DSig-Detect detects adversarial perturbations in images.
problem Adversarial attacks degrade image model performance.
method 2D-signature embedded semi-supervised framework using rough path theory.
result 2DSig-Detect outperforms other methods in detecting adversarial perturbations.
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
FADO detects adversarial scenarios without stochastic methods.
problem Adversarial scenarios where stochastic methods are not applicable.
method Machine learning and online learning theory-based deterministic detection.
result FADO provides theoretical guarantees for detection performance.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
problem Computational hardness of hypergraphic planted clique detection.
method No specific method mentioned; focuses on conjecturing equivalence.
result Equivalence of computational hardness between HPC and PC detection.
Real-time fuel leakage detection framework MOCPD improves accuracy.
problem Early detection of fuel leakage to prevent hazards and losses.
method Memory-based Online Change Point Detection (MOCPD) framework.
result MOCPD outperforms baseline methods in detection accuracy.
Study on detecting hierarchical community structures in networks.
problem Detecting hierarchical community structures in networks.
method Analysis of planted hierarchies of partitions in networks, identifying additional detectability phases.
result There are additional phases in which the presence of multiple consistent partitions can either help or hinder detection of hierarchical structures.
QuickStop detects misinformation quickly using Markov models.
problem Real-time detection of misinformation.
method Markov model for probabilistic information spreading; optimal stopping algorithm.
result QuickStop outperforms existing algorithms in accuracy and detection time.
Detects change-points in similarity networks to identify anomalous nodes.
problem Detecting changes in network structure that affect node similarity.
method Sequential node-wise average similarity measures for change detection; community detection for anomaly isolation.
result Simple sequential procedure effectively identifies change-points and anomalous nodes.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
problem Challenges in automated mitotic nucleus detection in breast cancer histopathological images.
method Adapted Mask R-CNN for deep object detection, initially selects candidate regions with maximum recall, refines them with multi-object loss function.
result Improved discrimination ability (F-score of 0.86) and significant precision (0.86) for mitotic nuclei compared to two-stage models.
ECAD detects anomalies without data exchangeability, improving traffic flow detection.
problem Detecting anomalies in spatio-temporal data with missing values.
method ECAD uses conformal prediction to wrap around any regression algorithm, controlling Type-I error without data exchangeability.
result ECAD outperforms other methods in detecting anomalous traffic flow.
S4ND detects lung nodules faster and more accurately.
problem Efficient lung nodule detection from CT scans.
method Single-Shot Single-Scale 3D Convolutional Neural Network (CNN) trained end-to-end.
result S4ND outperforms state-of-the-art methods in terms of efficiency and accuracy.
PAC-Wrap provides provable guarantees for semi-supervised anomaly detection.
problem Ensuring reliable anomaly detection in safety-critical applications.
method PAC-Wrap wraps around existing anomaly detection methods to provide PAC guarantees.
result PAC-Wrap effectively provides rigorous guarantees for various anomaly detectors.
This paper offers a distribution-free method for post-detection changepoint localization.
problem Locating the exact time of a change in distribution after a sequential detection procedure.
method A distribution-free framework using conformal test martingales for sequential change detection and post-detection inference.
result Valid post-detection coverage guarantees and non-asymptotic bounds on confidence set size.
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.
The study defines backdoor detection in ML and proves its infeasibility.
problem Backdoor detection in machine learning systems.
method Formal statistical definition and analysis of feasibility.
result Backdoor detection is impossible except for very small alphabet sizes.
Research tackles novelty detection for mixed-type data, proposing probabilistic methods.
problem Detect anomalies in mixed-type datasets like numerical and categorical data.
method Experimental comparison of methods, probabilistic nonparametric model, autoencoder-based model.
result Developed robust methods for mixed-type data novelty detection.
Detects data drift in deep learning models using neural embeddings.
problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.
GANs improve anomaly detection accuracy.
problem Detecting unseen anomalies is challenging.
method Adversarial training of GANs.
result Remarkable results on anomaly detection.
New method detects anomalies using GAN with minimum likelihood regularization.
problem Detecting abnormal events in various domains.
method Generative Adversarial Networks with minimum likelihood regularization.
result Significant improvement over other methods on Cifar10 and UCI datasets.
End-to-end method learns geometry and appearance for multi-view object detection.
problem Challenges in multi-view object detection, including viewpoint, lighting, and scale variability.
method Jointly learns multi-view geometry and warping for robust cross-view object detection.
result Superior performance compared to baselines on a new street-level panorama data set.
Detecting Relative Anomalystat.ML
Detects anomalies relative to typical observations.
problem Common anomaly detection methods fail for frequent anomalies.
method Relative anomaly detection, considering location relative to typical observations.
result Effective for frequent anomalies, computationally feasible, real-time detection.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
problem The challenge of detecting data distribution changes (drift) in real-time systems.
method Developed adversarial data streams to show weaknesses in existing drift detection schemes.
result Demonstrated that common drift detection methods can be fooled by adversarial data streams.
It follows from earlier work of Silver-Williams and the authors that twisted Alexander polynomials detect the unknot and the Hopf link. We now show that twisted Alexander polynomials also detect the trefoil and the figure-8 knot, that twisted Alexander polynomials detect whether a link is split and that twisted Alexand…
New method detects OOD samples using neural network trajectories.
problem Lack of comprehensive layer exploration in OOD detection.
method Functional data perspective, analyzing sample trajectories through multi-layer classifier.
result Empirically validated as effective compared to state-of-the-art methods.
Multimodal deep learning improves flaw detection in software programs.
problem Current flaw detection relies on single software representations.
method Adapted multimodal deep learning models for flaw detection.
result Multimodal models outperform traditional deep learning models.
Proposes OCGNN for detecting anomalies in graph data.
problem Detecting anomalies in graph-structured data.
method One Class Graph Neural Network (OCGNN) combining Graph Neural Networks and one-class classification.
result Significant improvements in anomaly detection compared to baselines.
SPI detects anomalies using privileged information from training data.
problem Unsupervised anomaly detection in the absence of labeled test data.
method Constructs density estimates in privileged space and transfers them to anomaly scoring space.
result Significant improvement in anomaly detection performance with privileged information.
A novel semi-supervised outlier detection model detects anomalies with few labels.
problem Efficiently detecting group anomalies with limited labeled data.
method RCC-Dual-GAN model that combines RCC and M-GAN components for semi-supervised outlier detection.
result Significantly improved accuracy in outlier detection with few labeled anomalies.
A method detects vehicles far from tunnel CCTV using AI.
problem Tunnel CCTV's height limits detection of far-away vehicles.
method Object detection algorithm with inverse perspective transform.
result Deep learning model trained on warped images detects vehicles more accurately.
Model detects exercise fatigue with high accuracy.
problem Detecting exercise fatigue in real-time.
method Feature extraction using AHP, machine learning.
result 98.65% accuracy in detecting exercise fatigue.
New method detects changes in data streams efficiently.
problem Quickest change-point detection in data streams.
method Inductive Conformal Martingales for quickest change-point detection.
result Inductive Conformal Martingales are efficient under general conditions.
Paper improves anomaly detection and categorization in multi-cloud environments.
problem Differentiating among different types of attacks for better defense.
method Used supervised machine learning techniques (LR and RF) on a public dataset.
result More than 99% detection accuracy and 93.6% categorization accuracy.
The paper explores how model complexity affects OOD detection performance.
problem Ensuring reliability and safety of machine learning systems through OOD detection.
method Investigates the relationship between model capacity and OOD detection performance using empirical and theoretical analysis.
result The Double Descent phenomenon is observed in post-hoc OOD detection, indicating that overparameterization can enhance OOD detection.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
Summarizes changepoint detection models and their applications.
problem Detect abrupt changes in time-series data.
method Summarizes and analyzes traditional and alternative changepoint detection models.
result Explores future research directions in changepoint detection.
Improved YOLOv5 model detects mask-wearing with enhanced accuracy.
problem Detecting mask-wearing in high traffic public places.
method Improved YOLOv5l with Multi-Head Attentional Self-Convolution, Swin Transformer Block, I-CBAM module, and enhanced feature fusion.
result 1.1% improvement in mAP(0.5) and 1.3% improvement in mAP(0.5:0.95) compared to YOLOv5l.
Learning to Detectcs.IT
Deep neural networks improve MIMO detection accuracy and efficiency.
problem Improving MIMO detection accuracy and efficiency.
method Introduced two deep architectures: a standard fully connected network and a DetNet designed for MIMO detection.
result Achieved state-of-the-art performance with low computational requirements.