Detects ghostwriting in high school assignments.
problem Increasing problem of students hiring ghostwriters.
method Trained deep neural networks on large dataset.
result Achieved 0.875 accuracy and 0.947 AUC score.
On-device research index
arXiv research
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168,932 papers · 148 categories
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48 results for “assignment detection”
SoccerCPD detects tactical changes in soccer matches using spatiotemporal tracking data.
problem Detecting consistent team formations in fluid sports like soccer.
method Two-step change-point detection: formation and role changes.
result Accurately detects tactical changes and estimates formation and role assignments.
We present a novel active learning algorithm for community detection on networks. Our proposed algorithm uses a Maximal Expected Model Change (MEMC) criterion for querying network nodes label assignments. MEMC detects nodes that maximally change the community assignment likelihood model following a query. Our method is…
Two spectral algorithms for community detection in graphs with covariates are compared.
problem Detecting community structure in graphs with covariates.
method Two model-based spectral algorithms are presented and compared.
result The second algorithm often better estimates block assignments by accounting for vertex covariates.
Solves partial assignment problems using random clique complexes.
problem Partial assignment problems, especially with severe occlusions and distortions.
method Formulate as matching random clique complexes, analyze k-skeletons, match adjacency matrices, consider geometric neighbourhoods.
result Outperforms diverse matching algorithms significantly.
Quantum entropy scoring improves robust mean estimation and outlier detection.
problem Robust mean estimation and outlier detection in high-dimensional data.
method QUE-scoring based on quantum entropy regularization.
result First algorithm with optimal error rates and nearly-linear running time for robust mean estimation.
One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is a widely used model, for which various estimation methods have been developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all no…
Causal Responder Detectionstat.ME
CARD detects treatment responders with machine learning and adjustment.
problem Identifying responders in non-random treatment settings.
method Conformal prediction, machine learning, propensity score adjustment.
result High power responder detection in various scenarios.
A new metric for detecting out-of-distribution samples using neural rendering models.
problem Difficulty in detecting out-of-distribution samples with existing deep generative models.
method Derive metrics for out-of-distribution detection using a neural rendering model.
result Lower likelihood of latent variables is assigned to out-of-distribution samples.
Paper introduces a simple method to assign uncertainty in contrastive learning models.
problem Contrastive learning models lack uncertainty measures.
method Trains a deep network to assign uncertainty based on representation variance.
result Deep uncertainty model improves anomaly detection and out-of-distribution classification.
A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual at…
Using Model-based Overlapping Seed Expansion to detect highly overlapping community structurephysics.soc-ph
As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms tends to …
UN-AVOIDS visualizes and detects anomalies without needing labeled data.
problem The need for a unified framework to visualize and detect anomalies.
method UN-AVOIDS is an unsupervised, nonparametric approach that transforms data into a new space (NCDF) for both visualization and detection.
result UN-AVOIDS assigns invariant anomalous scores and achieves high AUC in detecting anomalies.
New method uses batch normalization to improve OoD detection.
problem Out-of-distribution samples are not reliably detected by generative models.
method Proposes exploiting in-batch dependencies for OoD detection.
result Empirical results show improved robustness for high-dimensional images.
SVD-RND detects blurred images better than conventional methods.
problem Blurred images can fool conventional OOD detection schemes.
method Constructs a novel RND-based detector that uses blurred images during training.
result SVD-RND outperforms baseline detectors in various domains.
A new method detects anomalous sounds using self-supervised learning.
problem Detecting anomalous sounds in noisy data.
method Flow-based self-supervised density estimation using Normalizing Flows.
result The method improves anomaly detection performance significantly.
Normalizing flows fail to detect OOD data due to learning local pixel correlations.
problem Detecting out-of-distribution data in machine learning systems.
method Investigated why normalizing flows fail to distinguish between in- and out-of-distribution data, and modified flow architecture to improve OOD detection.
result Modifying flow architecture can improve OOD detection by biasing the flow towards learning semantic structure of the target data.
Improved anomaly detection using adversarial mirrored autoencoders.
problem Detecting out-of-distribution samples in machine learning.
method Adversarial Mirrored Autoencoder (AMA) with latent space regularization.
result AMA improves anomaly detection performance on OOD detection benchmarks.
Proposes Likelihood Regret for VAEs to improve OOD detection.
problem VAEs can assign high likelihoods to OOD samples, making traditional likelihood thresholds unreliable.
method Introduces Likelihood Regret, a new OOD score for VAEs.
result Empirical results show Likelihood Regret outperforms existing methods for VAEs.
A new method assigns anomaly scores to features for better interpretation.
problem Interpreting anomaly scores from feature attributions.
method Proposes a characteristic function to attribute anomaly scores using Shapley value.
result Demonstrates the potential utility of the proposed attribution methods.
New method detects anomalies in time series data using HMMs.
problem Detect anomalies in discrete time series data.
method HMM gauge likelihood analysis (HMM GLA) using Baum-Welch algorithm and clustering.
result Successfully detects anomalies in synthetic and real-world syslog data.
New MIP approach for efficient change-point detection.
problem Offline multiple change-point detection in data streams.
method Mixed-integer programming (MIP) for globally optimal PWL fitting.
result Provable tighter relaxations for segment assignment variables.
BPVAE enhances VAE robustness to OOD inputs.
problem VAEs struggle with OOD detection, assigning higher likelihoods to some OOD samples.
method Combines VAE with two independent priors: training dataset and simple dataset.
result BPVAE outperforms standard VAEs in OOD detection and generalization.
It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We…
Proposes a method for anomaly detection with inexact labels.
problem Handling anomaly detection with inexact labels.
method Trains an anomaly score function using a neural network-based unsupervised method, maximizing the inexact AUC.
result Improves anomaly detection performance with inexact labels and outperforms existing methods.
Machine learning models encounter Out-of-Distribution (OoD) errors when the data seen at test time are generated from a different stochastic generator than the one used to generate the training data. One proposal to scale OoD detection to high-dimensional data is to learn a tractable likelihood approximation of the tra…
Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimesstat.ML
Paper proposes a method to improve variational inference for sparse networks.
problem Variational inference struggles with sparse networks, leading to inaccurate community detection.
method The method involves hard thresholding the posterior of community assignment after each iteration.
result The proposed method accurately recovers true community labels in sparse networks.
Improved isolation forest for better outlier detection.
problem Outlier detection in multivariate data.
method Random cuts across feature space, size of feature space, and point assignment information.
result Improved results in many situations without modifying tree structure.
New approach detects and ranks novel and developing cyber threats in Twitter.
problem Detecting and ranking novel and developing cyber threats in Twitter streams.
method Unsupervised machine learning approach focusing on novelty and trendiness.
result Ranking of cyber threat events based on importance score using extracted terms.
GRADE models evolving graph dynamics by learning node and community representations.
problem Lack of tools to study temporal community dynamics in evolving graphs.
method GRADE is a probabilistic model that learns evolving node and community representations via a random walk prior and variational inference.
result GRADE outperforms baselines in dynamic link prediction and dynamic community detection.
SoftCLT improves time series representation learning by soft contrastive loss.
problem Ignoring inherent correlations in time series leads to poor representation quality.
method SoftCLT introduces instance-wise and temporal contrastive loss with soft assignments.
result SoftCLT consistently improves various downstream tasks in time series learning.
Proposes a method to measure similarity between anomaly scores from different methods.
problem Difficulty in directly comparing anomaly detection methods.
method A measure based on extremal similarity in scoring distributions using a novel upper quadrant modeling approach.
result Demonstrates the ability to detect clusters of anomaly detection algorithms and achieve an accurate ensemble algorithm.
ABIForest improves anomaly detection using attention weights.
problem Anomaly detection in datasets.
method Attention mechanism integrated into Isolation Forest.
result ABIForest outperforms standard Isolation Forest on synthetic and real datasets.
NADS improves OoD detection accuracy by 57%.
problem Uncertainty in machine learning models when encountering out-of-distribution data.
method NADS searches for a distribution of architectures that perform well on a given task, optimizing a stochastic OoD detection objective.
result NADS achieves up to 57% improvement in accuracy over state-of-the-art methods.
Hierarchical VAEs detect out-of-distribution data by identifying low-level in-distribution features.
problem Out-of-distribution data often has in-distribution low-level features, leading to misleading likelihood estimates in deep generative models.
method Developed a fast, scalable, unsupervised likelihood-ratio score for out-of-distribution detection based on hierarchical variational autoencoders.
result Achieved state-of-the-art results on out-of-distribution detection across various data and model combinations.
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
problem Detecting and preventing bank transaction fraud caused by data breaches.
method A distributed alternating algorithm that assigns probabilities to different locations being compromised.
result BreachRadar achieves over 90% precision and recall in detecting compromised cards.
RSM-GAN detects anomalies in noisy, multivariate, seasonal time series.
problem Anomaly detection in complex, noisy multivariate time series with seasonality.
method Convolutional-LSTM layers and attention mechanism in a GAN framework.
result RSM-GAN outperforms existing models in false positive rate and precision.
Geometrically, high-likelihood regions in DGMs are unlikely to generate OOD data.
problem The paradox of high-likelihood OOD detection in deep generative models.
method Local intrinsic dimension estimation to identify high-likelihood regions that do not generate OOD data.
result A method pairing likelihoods and LID estimates for reliable OOD detection.
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.
This study benchmarks anomaly detection methods in a functional setup.
problem Efficient anomaly detection in high-frequency sensor data.
method Functional analysis approach for multivariate data.
result Comparison of anomaly detection methods on real datasets.
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimizationmath.OC
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outl…
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Modelscs.LG
New method uses KL divergence to detect out-of-distribution data effectively.
problem Flow-based models assign higher likelihoods to OOD data than ID data, making OOD detection challenging.
method Proposes a method leveraging KL divergence and local pixel dependence of representations for anomaly detection.
result Demonstrates effectiveness and robustness on prevalent benchmarks.
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexitycs.LG
Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the proba…
New analysis shows entropy term cancels out in likelihood-based OOD detection.
problem Curious likelihood values for out-of-distribution data.
method Decomposed average likelihood into KL divergence and entropy terms.
result Entropy term explains OOD behaviour and cancels out in expectation.
Algorithm solves job acceptance problem with random arrivals and values.
problem Decision-making under random job arrivals and values with limited acceptance.
method Proposes Non-Parametric Sequential Allocation (NPSA) algorithm.
result Expected reward converges to optimality as sample size increases.
In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders…
The knot invariant Upsilon, defined by Ozsvath, Stipsicz, and Szabo, induces a homomorphism from the smooth knot concordance group to the group of piecewise linear functions on the interval [0,2]. Here we define a set of related secondary invariants, each of which assigns to a knot a piecewise linear function on [0,2].…
In this letter, we propose enhanced factored three way restricted Boltzmann machines (EFTW-RBMs) for speech detection. The proposed model incorporates conditional feature learning by multiplying the dynamical state of the third unit, which allows a modulation over the visible-hidden node pairs. Instead of stacking prev…