Enhances network intrusion detection in noisy data.
problem Robustness against contaminated and noisy data inputs in network intrusion detection.
method Probabilistic Temporal Graph Network Support Vector Data Description (TGN-SVDD) model.
result Significant improvements in detection performance with synthetic noise.
In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multitask learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoof…
Paper uses VAEs to detect radar targets in complex noise.
problem Detecting radar targets in compound clutter and thermal noise.
method Proposes a VAE architecture to distinguish radar targets from various noise types.
result The VAE outperforms classical detectors in challenging noise conditions.
Improves signal detection in non-Gaussian noise using transformed data.
problem Signal detection in rank-one signal-plus-noise data matrices.
method Pre-transforming matrix entries and using linear spectral statistics for hypothesis testing.
result Sharp phase transition of largest eigenvalues in spiked rectangular matrices.
Optimal test for detecting signal in noisy matrix model.
problem Signal detection in noisy matrix models with unknown rank.
method Hypothesis test based on linear spectral statistics, optimal under Gaussian noise.
result Optimal test under Gaussian noise, improved with non-Gaussian noise.
Study benchmarks label noise detection methods, identifying best practices.
problem Label noise in real-world datasets affects model performance and evaluation reliability.
method Decomposed detection methods into label agreement, aggregation, and information gathering components; introduced a unified benchmark task and novel metric.
result In-sample probability aggregation with logit margin label agreement function achieves best results across scenarios.
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.
Noise analysis detects backdoors in DNNs quickly.
problem Detecting backdoors in DNNs trained on compromised data.
method Noise-infused image titration curves to quantify robustness and detect backdoors.
result DNNs with backdoors are more sensitive to noise and reveal their targets.
Novel model detects communities in noisy multilayer networks.
problem Understanding communities in noisy multilayer networks.
method Hierarchical variational inference for joint detection and typologizing.
result Discover communities of subjects with co-occurrent psychopathologies.
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
Noise Collector detects sparse signals from noisy data efficiently.
problem Efficiently detecting sparse signals from noisy high-dimensional data.
method Introduces Noise Collector (NC) matrix to solve augmented system Aρ+Cη=b0+e. result The l1-norm minimal solution of the augmented system has zero false discovery rate for any level of noise. Improves fault detection models in noisy data.
problem Poor generalization due to mislabeled samples in fault detection.
method Two-step framework: outlier identification and data modification.
result Significantly improved model generalization under label noise.
Bayesian methods detect clusters in noisy data more reliably.
problem Noisy data distorts traditional clustering methods, leading to unreliable results.
method Bayesian community detection using Minimum Description Length principle.
result Bayesian methods identify more robust clusters in noisy data.
UNHaP removes noise from physiological events using Hawkes processes.
problem Challenges in identifying true events from spurious ones in physiological signal analysis.
method UNHaP uses marked Hawkes processes to distinguish and unmix true events from noise.
result UNHaP significantly reduces false detection rates and enhances event understanding.
We analyze anomaly detection class imbalance using a solvable model.
problem Class imbalance hampers anomaly detection performance.
method We use an exact solution of the teacher-student perceptron model through replica theory.
result Optimal train imbalance is often different from 50%, influenced by intrinsic imbalance and data abundance.
Study detects signals in spiked Wigner models using log likelihood ratio.
problem Detecting signals in rank-one spiked Wigner models with non-Gaussian noise.
method Proved asymptotic normality of log likelihood ratio and computed error thresholds.
result Optimal signal-to-noise ratio threshold for reliable detection.
Paper studies signal detection in noisy environments with limited communication.
problem Signal detection in Gaussian noise with 1-bit communication constraints.
method Derives lower bounds and exhibits optimal testing strategies.
result Optimal distributed testing strategies attain the derived lower bound.
We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of multiple objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and ca…
We consider a setting, where the output of a linear dynamical system (LDS) is, with an unknown but fixed probability, replaced by noise. There, we present a robust method for the prediction of the outputs of the LDS and identification of the samples of noise, and prove guarantees on its statistical performance. One app…
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …
Improves detection of low-rank signals from noisy data matrices.
problem Statistical detection of low-rank signals in noisy data matrices.
method Entrywise pre-transforming data matrix for non-Gaussian noise, sharp phase transition thresholds, central limit theorem for linear spectral statistics, hypothesis test.
result Improves detection of low-rank signals from noisy data matrices, generalizing known results.
Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.
problem Noisy object re-identification in image datasets.
method Reframed Re-ID as a similarity task, using Siamese networks and Beta mixture models.
result Superior performance in noisy conditions compared to state-of-the-art methods.
Edge device deep learning improved with noise handling model.
problem Noise and low quality data degrade deep learning performance on edge devices.
method Mixture of Pre-processing Experts (MoPE) model with adversarially trained autoencoder.
result The MoPE model achieves better accuracy on noisy images without sacrificing clean image accuracy.
DynaCor detects noisy labels by learning from corrupted training signals.
problem Label noise in real-world datasets hinders model generalization.
method DynaCor introduces label corruption to indirectly simulate noisy labels and learns to distinguish clean from noisy instances.
result DynaCor outperforms state-of-the-art competitors in noisy label detection.
Method detects jumps in high-frequency order prices using local minima.
problem Detecting jumps in high-frequency order prices with noisy data.
method Developed methods to estimate, locate and test for jumps using local minima of best ask quotes.
result Consistently estimated jump sizes and times, established asymptotic properties of tests, and demonstrated faster convergence rates.
We consider the weak detection problem in a rank-one spiked Wigner data matrix where the signal-to-noise ratio is small so that reliable detection is impossible. We propose a hypothesis test on the presence of the signal by utilizing the linear spectral statistics of the data matrix. The test is data-driven and does no…
Study on signal-plus-noise decomposition in nonlinear spiked random matrices.
problem Nonlinear spiked random matrix models with rank-one signal and noise.
method Signal-plus-noise decomposition and phase transition analysis.
result Identified precise phase transitions in signal components at critical thresholds.
LAAT detects multiple low-density manifolds in noisy data.
problem Detecting multiple low-density manifolds in noisy data.
method Locally Aligned Ant Technique (LAAT) based on Ant Colony Optimization.
result LAAT recovers multiple manifolds in extremely noisy data.
Study mutual info for community detection with covariate and correlated networks.
problem Community detection with covariate and correlated networks.
method Asymptotic upper bound and MMSE matrix heuristic analysis.
result Explicit characterization of combined information effects.
Detects model misspecifications in causal models using observational data.
problem Identifying predictor variables with causal effects in misspecified models.
method Develops a general framework based on observational data distribution and proposes an algorithm for finite sample data.
result Identifies predictor variables for causal effects even in misspecified models.
New method improves false-/true-positive-rate estimation in fraud detection with noisy labels.
problem Estimating FPR/TPR in fraud detection with class-conditional label noise.
method Directly cleaning model's validation data to de-correlate cleaning error with model scores.
result Improves accuracy of FPR/TPR estimates, especially in asymmetric label noise scenarios.
This paper improves change-point detection for complex data streams using denoising score matching.
problem Timely identification of distributional shifts in high-dimensional, complex data streams.
method Score-based CUSUM change-point detection with denoising score matching.
result Denoising score matching enhances detection power by effectively controlling noise scale.
We develop an unsupervised, nonparametric, and scalable statistical learning method for detection of unknown objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes and sizes in the presence of nonparametri…
The paper proposes a method to detect and filter noisy or mislabeled data using pointwise mutual information.
problem Detecting and filtering noisy or mislabeled data in deep learning models.
method A mutual information-based framework quantifying statistical dependencies between inputs and labels.
result The method effectively filters low-quality samples, improving classification accuracy by up to 15%.
A new method detects small holes in noisy data.
problem Detecting small holes in high-density regions from noise.
method Robust Density-Aware Distance (RDAD) filtration, incorporating distance-to-measure concept.
result The RDAD filtration prolongs the persistences of small holes, making them distinguishable from noise.
Exact recovery method for community detection in Gaussian mixtures with dependent noise.
problem Community detection in Gaussian mixtures with dependent and heterogeneous noise.
method Maximum likelihood estimator (MLE) for constrained quadratic optimization problem, using Σ-whitened separation and local inequalities. result Sharp exact-recovery threshold and no-gap mechanism in the unknown-size setting.
Automatically detects and down-weights noisy samples in machine learning training.
problem Numerical noise in reference data hampers the accuracy of machine learning models.
method On-the-fly outlier detection using exponential moving average to identify and down-weight noisy samples.
result The method prevents overfitting and matches the performance of iterative refinement with reduced overhead.
Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.
problem Optimizing medical imaging systems by assessing their performance using the Ideal Observer model.
method Uses supervised learning methods, specifically convolutional neural networks, to approximate the Ideal Observer for joint signal detection and localization tasks.
result Supervised learning-based methods can approximate the Ideal Observer for joint signal detection and localization tasks, as shown by comparisons to MCMC and analytical methods.
Bayesian framework detects symmetries in chaotic dynamical systems.
problem Detecting symmetries in chaotic attractors for insights into dynamical system structure.
method Bayesian framework using Gibbs posterior constructed from Wasserstein distances.
result Bayesian framework accurately recovers symmetries under high noise and small sample sizes.
The presence of noisy instances in mobile phone data is a fundamental issue for classifying user phone call behavior (i.e., accept, reject, missed and outgoing), with many potential negative consequences. The classification accuracy may decrease and the complexity of the classifiers may increase due to the number of re…
Bayesian method improves deep learning for noisy EEG seizure detection.
problem Label noise in scalp EEG data hinders deep learning performance.
method Integrates domain knowledge into a Bayesian framework to inform deep learning models of label ambiguities.
result BUNDL enhances robustness of seizure detection systems under noisy label conditions.
Detect changes in noisy dynamical systems using empirical approximations and finite-sample bounds.
problem Change detection in noisy dynamical systems
method Partition-based empirical approximations and finite-state stationary distribution stability
result Finite-sample bound for empirical stationary density
This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing an anomalous input. Detecting anomalies is a critical component for multiple machine learning problems including detecting adversarial noise. More br…
New method uses tensor decomposition to improve noise reduction in machine fault detection.
problem Noise in acoustic signals hinders fault detection in industrial machines.
method Non-negative Canonical Polyadic (CP) decomposition for denoising spectral data.
result Improvement in unsupervised anomaly detection for machine fault detection.
Detects and corrects adversarial images using image processing.
problem Vulnerability of deep neural networks to adversarial attacks.
method Image processing operations to detect and correct adversarial images.
result The method effectively detects and corrects adversarial images.
Inserting label noise can improve model accuracy and fairness.
problem Improving model accuracy and fairness with noisy labels.
method Increasing label noise rates to balance and detect noisy instances.
result Inserting label noise can lead to more accurate and fair models.
Deep learning generates efficient change-point detection methods.
problem Detecting change-points in data with various types of change and data behavior.
method Train a neural network to automatically generate detection methods.
result Neural network-based methods are competitive and outperform standard methods in various noise conditions.
Paper tackles anomaly detection in SAR images without labeled data.
problem Anomaly detection in SAR images with speckle noise and spatial correlation issues.
method Self-supervised deep learning approach: despeckling, adversarial autoencoder, change detection.
result Method outperforms conventional algorithms in detecting anomalies in SAR images.