Optimizes noisy IS with better proposal densities.
problem Improving IS estimators with noisy data.
method Derives optimal proposal densities considering noise variance.
result Optimal proposals enhance IS estimators by focusing on noisy regions.
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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 “noisiness”
Class2Simi reduces noise in noisy label learning by transforming noisy class labels into noisy similarity labels.
problem Learning with noisy labels in supervised and unsupervised settings.
method Transforming noisy class labels into noisy similarity labels, training DNNs from noisy data pairs.
result The noise rate reduction is theoretically guaranteed, making it easier to handle noisy similarity labels.
Quantum neural networks can approximate noisy functions accurately.
problem Approximating noisy functions with quantum neural networks.
method Universal approximation theorem with error bounds for noisy quantum neural networks.
result Quantum neural networks can approximate noisy functions with precise error bounds.
Paper improves -SSC for noisy data by proving SDP and proposing Noisy-DR--SSC.
problem Noisy data and less restrictive subspace affinity in sparse subspace clustering.
method Proposes Noisy-DR--SSC, which projects data onto a lower dimensional space and then applies noisy -SSC.
result Theoretical guarantee on the correctness of noisy -SSC in terms of SDP on noisy data.
Method detects and relabels noisy image labels to improve DNN performance.
problem Label noise in training images harms DNN generalization.
method Identifies noisy labels based on predictive uncertainty changes over training.
result Iterative relabeling of noisy labels improves DNN performance.
Simple method improves deep learning with noisy labels.
problem Deep learning's overfitting to noisy labels.
method Adds a variance regularization term to penalize neural network's Jacobian norm.
result Achieves state-of-the-art performance with high noise tolerance.
ExpertNet uses noisy labels to improve deep learning robustness.
problem Improving deep learning robustness against noisy labels.
method ExpertNet framework combining Amateur and Expert models, iteratively learning from noisy labels and images.
result ExpertNet achieves robust classification with as little as 20-50% training data, outperforming state-of-the-art models.
Paper tackles noisy similarity labels for multi-class classification.
problem Learning multi-class classifiers from noisy similarity-labeled data.
method Proposes a method using a noise transition matrix to learn from noisy data.
result Demonstrates superior performance compared to state-of-the-art methods.
Proposes coreset method for robust training of neural networks with noisy labels.
problem Overfitting of neural networks trained with noisy labels.
method Selects weighted subsets (coresets) of clean data points to approximate low-rank Jacobian matrix.
result Gradient descent applied to coreset subsets does not overfit noisy labels.
PSDR improves robustness against noisy labels by penalizing KL divergence between similar inputs.
problem Robust training of DNNs in datasets with noisy labels.
method Introduces PSDR, a manifold regularizer that penalizes KL divergence between similar inputs.
result Significantly improves robustness against noisy labels on benchmark datasets.
MARVEL curbs memorization of noisy labels in deep nets.
problem Noisy labels degrade deep net performance.
method MARVEL tracks classification margins to identify and abandon noisy instances.
result MARVEL outperforms baselines on noisy datasets.
Paper explains why small-loss criterion works for learning from noisy labels.
problem Learning from noisy labels in deep learning with limited labeled data.
method Theoretical analysis and reformulation of the small-loss criterion.
result Theoretical explanation and reformulation of the small-loss criterion.
Consensus NN learns from noisy data only for medical image denoising.
problem Lack of clean training data for medical image denoising.
method Trains neural network using only noisy data by splitting and combining subsets.
result Improved performance on denoising medical images compared to existing methods.
Study how noisy labels affect semi-supervised learning.
problem Effect of noisy labels on semi-supervised learning performance.
method Proposed an algorithm derived from a continuous relaxation of the Maximum A Posteriori (MAP) estimator for a Degree Corrected Stochastic Block Model (DC-SBM).
result Our approach achieves promising performance even with very noisy labeled data.
CrossFilter tackles noisy labels in audio tagging.
problem Noisy labels in large audio datasets.
method CrossFilter framework using multiple representations and multi-task learning.
result Improves audio tagging performance on FSDKaggle2018 and FSDKaggle2019 datasets.
CNT leverages noisy targets to guide model learning.
problem Learning from noisy or incomplete labels.
method Conditioning model on noisy targets at inference time.
result Model focuses on simpler sub-problems and learns from easier examples first.
Improves classification accuracy with noisy labels using generative classifiers.
problem Handling noisy labels in large-scale datasets.
method Robust Generative Classifier (RoG) on top of pre-trained DNNs.
result Significantly improves classification accuracy with no re-training of the deep model.
Paper analyzes and improves DNNs trained with noisy labels.
problem Training deep neural networks with noisy labels.
method Characterize test accuracy as a function of noise ratio, apply cross-validation, and use Co-teaching strategy.
result Our strategy consistently improves DNNs' generalization performance.
CbMLC improves multi-label classification with noisy labels.
problem Evaluating multi-label classifiers with noisy labels.
method Context-Based Multi-Label Classifier (CbMLC) that handles noisy labels without additional supervision.
result CbMLC yields substantial improvements over previous methods in noisy label settings.
A new method simplifies noisy data filtering for CNNs.
problem Training CNNs with noisy labels is challenging.
method Joint Negative and Positive Learning (JNPL) combines NL+ and PL+ loss functions.
result Significantly simplifies the pipeline, achieving state-of-the-art accuracy.
Boosting classifiers improve accuracy with noisy inputs.
problem Noisy communication or computation degrades boosting classifier accuracy.
method Optimize resource allocation for base classifiers based on importance metrics.
result Optimized noisy boosting classifiers are more robust than bagging.
Paper shows how noisy data can improve robust decision-making.
problem The challenge of noisy data in decision-making.
method Distributionally robust optimization (DRO) with a novel ambiguity set construction.
result Noisy data can lead to more robust and equitable decisions.
ROVAE uses noisy pairwise comparisons to disentangle factors in VAEs.
problem Disentangling factors in VAEs requires an inductive bias.
method Robust Ordinal VAE (ROVAE) incorporates noisy pairwise ordinal comparisons to disentangle factors.
result ROVAE outperforms existing methods and is more robust to noisy comparisons.
Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, however they tend to contain inaccurate labels that are termed as noisy labels. Training on such noisy labeled datasets causes performance de…
A method to train deep neural networks on noisy labeled data.
problem Training deep neural networks on noisy labeled data causes performance degradation.
method A noise-tolerant training algorithm that simulates actual training with synthetic noisy labels.
result The proposed method outperforms state-of-the-art baselines on noisy CIFAR-10 and Clothing1M datasets.
The paper tackles learning true rankings from noisy, incomplete data.
problem Learning true rankings from incomplete and noisy data.
method Introduces a selective Mallows model for noisy rankings and derives upper and lower bounds on sample complexity.
result Strong asymptotically tight bounds on sample complexity for learning complete rankings and top-k rankings.
Paper proposes SL to improve DNN learning with noisy labels.
problem Learning with noisy labels in deep neural networks.
method Symmetric Cross Entropy (SL) with Reverse Cross Entropy (RCE).
result SL outperforms state-of-the-art methods on various datasets.
Estimates linear model from noisy covariates and instruments using spectral regularization.
problem Estimating a linear model from many noisy covariates and instruments.
method Two-stage least squares with spectral regularization of canonical correlations.
result Upper and lower bounds on estimation error, proving optimality of the method with noisy data.
A corrected EI acquisition function handles noisy observations in Bayesian optimization.
problem Noisy observations in Bayesian optimization.
method Proposes a modified expected improvement (EI) acquisition function that incorporates covariance information from the Gaussian Process model.
result Achieves a sublinear convergence rate on cumulative regret bound under heteroscedastic observation noise.
Paper proposes an alternative to anchor points for learning with noisy labels.
problem Learning with noisy labels is challenging due to inaccurate labels.
method Estimates transition matrix using clusterability condition and noisy labels.
result Estimation of transition matrix is more accurate and efficient than anchor points.
Improved neural network inference with eigenvalue correction.
problem Inference of flexible variational posteriors is computationally expensive.
method Eigenvalue correction to matrix-variate Gaussian posterior.
result Empirically, the method outperforms existing algorithms.
Bayes classifier cannot be learned from noisy labels without knowing noise distribution.
problem Learning a Bayes classifier from noisy labels when the noise distribution is unknown.
method Demonstrates the identifiability issues and proposes a simple algorithm for learning the Bayes decision rule.
result The Bayes decision rule is generally unidentified and cannot be learned without knowing the noise distribution.
Simple k-NN filtering improves model accuracy on noisy labels.
problem Training models with noisy labels reduces performance and is hard to identify.
method A simple -nearest neighbor-based filtering approach on the logit layer.
result Improves model accuracy compared to recent methods.
Paper tackles noisy labels for non-decomposable performance measures.
problem Learning from noisy labels for non-decomposable performance measures.
method Designs algorithms for multiclass non-decomposable performance measures using Frank-Wolfe and Bisection methods, corrected for class-conditional noise.
result Noise-corrected algorithms are Bayes consistent, converging to optimal performance.
Paper proposes a new meta-learning approach for correcting noisy labels.
problem Learning with noisy labels in machine learning models.
method Meta-learned instance re-weighting approach extended to label correction problem.
result Proposed MLC (Meta Label Correction) framework achieves large improvements over previous methods.
This work explores how neural network architecture affects robustness to noisy labels.
problem The impact of neural network architecture on robustness to noisy labels.
method Formal framework connecting robustness to architecture alignments, measured by predictive power in representations.
result Network robustness to noisy labels improves when its architecture is more aligned with the target function.
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, the label noises have been treated as statistical outliers, and approaches such as importance re-weighting and bootstrap have been proposed …
Study efficient graph optimization with noisy data.
problem Optimizing functions on graphs with noisy observations.
method Best-arm identification and simulated annealing variants.
result Near-optimal solutions found with small query numbers.
Peer loss functions learn from noisy labels without noise rate specification.
problem Learning from noisy labels without knowing noise rates.
method Introduced peer loss functions within ERM framework.
result Peer loss functions lead to optimal or near-optimal classifiers.
Paper introduces a noisy-labeled audio tagging challenge.
problem Acoustic mismatch and noisy labels in audio tagging.
method Large dataset with minimal supervision, convolutional neural network baseline.
result Demonstrates effectiveness of minimal supervision in noisy conditions.
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channelsstat.ML
The paper analyzes generalization of noisy iterative algorithms using communication theory.
problem Generalization of models trained by noisy iterative algorithms under different distributions.
method Connecting noisy iterative algorithms to additive noise channels in communication theory.
result Distribution-dependent generalization bounds for noisy iterative algorithms.
Survey on deep learning robust training methods for noisy labels.
problem Dealing with noisy labels in deep learning models.
method Comprehensive review of 62 robust training methods categorized by their approach.
result Analysis of noise rate estimation and evaluation methodologies.
LEC prevents deep nets from memorizing noisy examples.
problem Deep nets overfit to noisy data.
method LEC removes noisy examples based on an ensemble of perturbed networks.
result LTEC outperforms state-of-the-art on noisy MNIST, CIFAR-10, and CIFAR-100.
Study real-world noisy labels from human annotations for better understanding.
problem Understanding and modeling real-world label noise in machine learning.
method Developed two new benchmark datasets (CIFAR-10N, CIFAR-100N) with human-annotated real-world noisy labels.
result Real-world noisy labels exhibit instance-dependent patterns, not class-dependent as previously assumed.
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.
New methods ensure fairness in noisy protected groups.
problem Noisy or biased protected group information complicates fairness audits.
method Robust optimization techniques to enforce fairness on true groups.
result Robust approaches achieve better true group fairness guarantees.
New model handles noisy labels in semi-supervised classification.
problem Noisy class labels in classification tasks.
method M-VAE: A semi-supervised deep generative model that explicitly models noisy labels.
result M-VAE performs better than models ignoring label noise.
Rotational invariant estimator for general noisy matricescond-mat.stat-mech
We investigate the problem of estimating a given real symmetric signal matrix from a noisy observation matrix in the limit of large dimension. We consider the case where the noisy measurement comes either from an arbitrary additive or multiplicative rotational invariant perturbati…