Improves community detection in directed networks with theoretical guarantees.
problem Degree heterogeneity affects community detection in directed networks.
method Introduced D-SCORE algorithm and established theoretical guarantees for Directed-DCBM.
result Established theoretical guarantees and provided improvements for D-SCORE.
New method detects latent common causes from observational data.
problem Detecting latent common causes in observational data.
method Modified causal discovery algorithms to detect latent common causes.
result Successfully detects latent common causes in various noise regimes and real data.
The paper presents a method for sound event localization and detection using CRNN models.
problem Sound event localization and detection in complex environments.
method Consecutive ensemble of CRNN models for estimating event onset, offset, direction of arrival, and classification.
result The proposed method outperforms other participants in the DCASE2019 task3.
Paper introduces AIF for anomaly detection with variable feature sensitivity.
problem Lack of variable sensitivity in anomaly detection methods.
method Extended Isolation Forest with feature sensitivities (Anisotropic Isolation Forest).
result AIF enables anomaly detection with controllable sensitivity to different features.
Bi-directional Curriculum Learning improves graph anomaly detection by considering both homogeneity and heterogeneity.
problem Existing graph anomaly detection methods often ignore the different contributions of nodes to training.
method Introduces Bi-directional Curriculum Learning (BCL) to optimize GAD methods by considering both homogeneity and heterogeneity of nodes.
result Extensive experiments show that BCL significantly improves the performance of GAD anomaly detection models.
KOD detects outliers in high-dimensional data.
problem Challenges in outlier detection in high-dimensional settings.
method Kernel transformation followed by projection pursuit approach with ensemble of directions and result combination.
result Empirical evaluations show effectiveness on various datasets.
New method detects rock type changes in real-time during drilling.
problem Detecting rock type changes in time during directional drilling.
method Combines machine learning and change detection procedures.
result Significant reduction in change detection delay and false positives.
ZDP detects drift in large language models without labels, proving key theorems and metrics.
problem Detecting drift in large language models without task labels or output evaluations.
method Zero-Direction Probing (ZDP) framework based on null directions of transformer activations, proving theoretical guarantees.
result Proves the Variance--Leak Theorem, Fisher Null-Conservation, Rank--Leak bound, and logarithmic-regret guarantee.
iSearch uses innovation directions for robust PCA and outlier detection.
problem Robust PCA and outlier detection in data with outliers.
method iSearch uses innovation directions to compute optimal data points and identify outliers.
result iSearch provides robust PCA and outlier detection with performance guarantees.
Detects adversarial directions to make reinforcement learning policies more robust.
problem Adversarial attacks exploit non-robust directions in reinforcement learning policies, leading to instability.
method Local quadratic approximation of deep neural policy loss to identify non-robust directions.
result Provides a theoretical basis for distinguishing safe from adversarial observations.
A new framework uses information theory to detect anomalies in images without labeled data.
problem Detect anomalies in images without labeled data.
method A direct objective function using information theory to maximize the distance between normal and anomalous data.
result The proposed framework significantly outperforms state-of-the-arts on multiple benchmark datasets.
Unsupervised method discovers interpretable directions in GAN latent space.
problem Discovering interpretable directions in GAN latent space without supervision.
method Model-agnostic procedure to identify directions corresponding to semantic manipulations.
result Findings include directions for background removal and competitive saliency detection performance.
Machine learning detects drilling anomalies, reducing accidents and costs.
problem Detecting and preventing accidents during directional drilling.
method Time-series comparison using machine learning and Gradient Boosting classification.
result The model detects half of the anomalies with about 0.53 false alarms per day.
Generalized change-point detection using various binary models.
problem Discovering changes in time series distribution.
method Direct density ratio estimation with Gradient Boosting over Decision Trees and Neural Networks.
result Proposed methods outperform classical RuLSIF algorithm.
New method for community detection in sparse directed SBMs with exact recovery guarantees.
problem Exact recovery in sparse directed SBMs, especially with growing communities.
method Two-stage procedure: neighborhood-smoothing followed by K-means clustering. result Exact recovery of all community labels with probability tending to one under mild sparsity and separation conditions.
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.
Community detection has been one of the central problems in network studies and directed network is particularly challenging due to asymmetry among its links. In this paper, we found that incorporating the direction of links reveals new perspectives on communities regarding to two different roles, source and terminal, …
Develops deep neural networks for accurate seizure detection.
problem Seizure detection using EEG signals and brain connectivity.
method Combines EBC, DMNN, MENN, and MFNN methods.
result DMNN achieves highest accuracy (99.43%).
Online CPD for weighted and directed graphs using RDPG model.
problem Monitoring and detecting changes in weighted and directed graph data.
method Spectral embeddings of RDPG models for online updates and error-rate control.
result A lightweight online CPD algorithm with improved detection resolution and delay.
Paper detects anomalous edges in social networks using edge exchangeability.
problem Detecting anomalous edges in directed social networks.
method Exploits edge exchangeability and uses conformal prediction theory.
result Proposed anomaly detector has a guaranteed upper bound for false positives.
We show that the cobordism groups of negative codimensional folds maps contain direct sums of stable homotopy groups of Thom spaces of vector bundles like the circle and the infinite dimensional projective space. We give geometrical invariants which detect these direct summands.
New framework detects directional influence in multivariate time series.
problem Detecting directional influence in multivariate time series.
method Order-constrained spectral non-invariance.
result Unique diagnostic functional for directional influence.
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.
Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e.g. portfolio monitoring, insurance, environmental risk management and anomaly detection. One convenient (non-parametric) characterization of extremal de…
Resultant improves likelihood-based U-OOD detection across various tasks.
problem Improving likelihood-based U-OOD detection performance.
method Resultant combines post-hoc prior and dataset entropy-mutual calibration techniques.
result Resultant achieves new state-of-the-art U-OOD detection performance.
Survey of deep learning applications in traffic congestion detection, prediction, and alleviation.
problem Improving transportation network service by detecting, predicting, and alleviating traffic congestion.
method Comprehensive review of deep learning applications in transportation, focusing on challenges specific to congestion.
result Identification of challenges and gaps in current research on deep learning for traffic congestion.
Study assesses risk of upward lightning at wind turbines using direct measurements and machine learning.
problem Risk underestimation of upward lightning at wind turbines due to limited detection by current standards.
method Direct UL measurements linked to meteorological reanalysis data using random forests.
result Risk maps based on case study events show high probabilities coincide with actual UL events.
Proposes TNPM for better node popularity in directed and bipartite networks.
problem Lack of node popularity consideration in community detection of directed and bipartite networks.
method Two-Way Node Popularity Model (TNPM) with Delete-One-Method (DOM) and Two-Stage Divided Cosine Algorithm (TSDC).
result Improved estimation accuracy and computational efficiency demonstrated through real-world applications.
Detects BGP routing anomalies using bursty announcement patterns.
problem Detecting disruptive routing updates in the Internet.
method Burstiness measure of inter-arrival times around disruptive update dates and times.
result High recall and better precision in detecting anomalous incidents.
In the bordered Floer theory, gluing thickened torus of positive meridional Dehn twist to the boundary of a knot complement result in the knot complement of increased framing. For a fixed knot K, we construct a direct system of positively framed knot complements and study the direct limit. We also study the morphism sp…
Novel technique detects adversarial samples in face recognition models.
problem Widespread adversarial sample attacks on DNN models.
method Bi-directional correspondence inference between attributes and internal neurons to identify critical neurons.
result 94% detection accuracy for 7 different kinds of attacks with 9.91% false positives.
A drone-based MOT algorithm tracks vehicles using neural network detections and TPMBM filter.
problem Tracking multiple vehicles from drone-mounted cameras.
method Neural network for object detection, TPMBM filter for trajectory estimation, von-Mises Fisher distribution for DOA.
result TPMBM filter optimally estimates vehicle trajectories.
Novel CGTF model for recommender systems and community detection from coupled graphs and tensors.
problem Lack of effective methods for analyzing multiple information repositories with graph side information.
method Coupled Graph-Tensor Factorization (CGTF) with ADMM for nonnegative factor recovery.
result CGTF model successfully detects communities even with missing graph links.
Detects underspecification in pre-trained models using local ensembles.
problem Underspecification in pre-trained models where many predictors are consistent with training data.
method Uses local second-order information to approximate prediction variance across an ensemble of models.
result Capable of detecting underspecification in pre-trained models on test data.
ALAD uses GANs to detect anomalies in complex data.
problem Effective anomaly detection for complex, high-dimensional data.
method Adversarially learned features derived from bi-directional GANs.
result Significantly improved anomaly detection performance.
Khovanov homology detects essential surfaces in knot complements.
problem Detecting essential surfaces in knot complements.
method Identifying Khovanov chain complex generators with normal surfaces using ideal triangulations.
result Colored Khovanov homology detects essential surfaces as in slope conjectures.
Deep learning improves skin cancer detection.
problem Early detection of skin cancer is challenging due to lack of professionals and instruments.
method Overview of recent deep learning models applied to skin cancer detection.
result Deep learning models enhance skin cancer detection accuracy.
Detects lead-lag clusters in US equity market time series.
problem Identifying lead-lag relationships in multivariate time series.
method Directed network clustering of lead-lag relationships.
result Validated on US equity market data, detects statistically significant lead-lag clusters.
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela…
This is a survey paper of author's results on cobordism groups and semigroups of fold maps and simple fold maps. The results include: establishing a relation between fold maps and immersions through geometrical invariants of cobordism classes of fold maps and simple fold maps in terms of immersions with prescribed norm…
SVDD and Deep SVDD improve radar target detection in clutter.
problem Clutter and thermal noise degrade classical radar detection methods.
method Support Vector Data Description (SVDD) and Deep SVDD for one-class learning.
result SVDD and Deep SVDD outperform traditional methods on simulated radar data.
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.
Survey of deep learning methods for medical anomaly detection.
problem Medical anomaly detection using machine learning.
method Thorough review of deep learning techniques across various medical domains.
result Comparison and contrast of deep learning models and their limitations.
We introduce a new unsupervised anomaly detection ensemble called SPI which can harness privileged information - data available only for training examples but not for (future) test examples. Our ideas build on the Learning Using Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17], which we extend …
Detects malicious accounts in permissionless blockchains using graph properties and ML.
problem Identifying and classifying malicious accounts in permissionless blockchains.
method Temporal graph properties, ML algorithms (ExtraTreesClassifier, K-Means), cosine similarity, behavior change analysis.
result ExtraTreesClassifier performs best in detecting malicious accounts on Ethereum blockchain.
We accelerate Bayesian inference for neutrino physics experiments by 100-60x.
problem Complex posterior geometries in multi-dimensional parameter spaces.
method GPU acceleration, automatic differentiation, neural-network-guided reparameterization.
result Significant performance improvements in Bayesian inference for direct detection experiments.
Spectral clustering for directed graphs using likelihood estimation.
problem Clustering directed graphs with edge directions.
method Maximum likelihood estimation on stochastic block models.
result Significant performance gains over existing methods.
Constructs Lie algebras from labeled directed graphs and identifies properties of these algebras.
problem Constructing and analyzing Lie algebras from labeled directed graphs.
method Using labeled directed simple graphs to construct 2-step nilpotent Lie algebras, identifying ideals and subalgebras through special subgraphs, and proving isomorphisms based on label occurrences.
result Lie algebras depend only on the underlying undirected graph if all edges are labeled uniquely.