Paper proposes BCNMCC for system identification with noisy input.
problem System identification with noisy input and impulsive output noise.
method Introduces BCV to NMCC algorithm to compensate for input noise bias.
result BCNMCC outperforms other algorithms in noisy input conditions.
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.
NES improves robust optimization with noisy inputs.
problem Finding robust optima in problems with input and measurement noise.
method Noisy-Input Entropy Search (NES) acquisition function based on Gaussian process modeling.
result NES reliably finds robust optima, outperforming existing methods.
End-to-end speech recognition using EEG without speech input.
problem Speech recognition without direct speech input.
method Implemented attention model and CTC-based ASR systems for EEG signals; fused EEG with noisy speech features.
result Demonstrated end-to-end speech recognition using EEG signals.
Paper shows continuous speech recognition with EEG features, no speech input.
problem Continuous speech recognition with limited vocabulary and noisy/no speech input.
method Connectionist temporal classification (CTC) model, EEG features, new deep learning architecture.
result Continuous speech recognition achieved on limited vocabulary with noisy/no speech input.
Bayesian optimisation tackles uncertain inputs in noisy function evaluations.
problem Optimizing functions with uncertain query locations and noisy outcomes.
method Proposes a UCB algorithm for BO with uncertain inputs, using Gaussian process models.
result Theoretical and experimental results show the proposed algorithm outperforms conventional methods.
There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and …
Study shows noisy historical data can still predict future text classification well.
problem Challenges in text classification with noisy, historical data.
method Examined how performance metrics on noisy data reflect future model performance.
result Noisy training data can be used to build effective prediction models for cleaner inputs.
Student-teacher learning improves generalization with noisy inputs.
problem Transfer knowledge from clean inputs to noisy inputs.
method Analyzes student-teacher learning using deep linear networks and experiments with nonlinear networks.
result Three factors are vital for success: zero training loss, teacher knowledge, and feature decomposition.
Paper corrects deep learning for noisy labels.
problem Overfitting to imperfectly labeled data.
method Distribution correction approach to handle noisy inputs.
result Significantly higher accuracy compared to alternative methods.
Learning theory for linear systems with compositional inputs.
problem Training linear system operators with unknown variables constrained to non-negativity and unity.
method Bayesian inversion method for inferring unknown variable from noisy linear system output.
result Quantified uncertainty in trained operator and convergence rates for various cases.
Gradient descent learns ReLU networks with Gaussian inputs and noisy outputs.
problem Learning one-hidden-layer ReLU networks with Gaussian inputs and noisy outputs.
method Gradient descent with tensor initialization for empirical risk minimization.
result Gradient descent converges to ground-truth parameters at a linear rate up to statistical error.
Gen-CUDE is a neural network for denoising noisy channels.
problem Denoising in finite-input, general-output noisy channels.
method Unsupervised neural network trained on noisy data.
result Gen-CUDE achieves better denoising results than other methods.
The study examines denoising and noisy-input regression under distribution shift, revealing double descent behavior and insights for data augmentation.
problem Understanding denoising in machine learning, especially under noisy inputs and distribution shift.
method Theoretical analysis of supervised denoising and noisy-input regression, considering low-rank data and proportional regime.
result The test error exhibits double descent under general distribution shift, indicating that overfitting the noise can be benign, tempered, or catastrophic.
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.
Deep networks can interpolate noisy data without losing generalization.
problem Characterizing the relationship between interpolation and generalization in overparameterized deep networks.
method Analyzing the loss landscape of neural network functions over volumes around training data points, varying model parameters and training epochs.
result Loss sharpness in the input space follows a double descent, with large models predicting noisy targets over larger volumes around training data points.
PGMs and GNNs differ in capturing network data; PGMs outperform GNNs in noisy and heterophily scenarios.
problem Comparing PGMs and GNNs in network data.
method Link prediction task with synthetic and real networks; three experiments on input features, noise, and heterophily.
result PGMs outperform GNNs in noisy and heterophily scenarios.
A hybrid neural network improves robustness in estimating vehicle parameters from noisy data.
problem Estimating parameters of a mechanical vehicle model from noisy acceleration data.
method Introduced a convolutional neural network with two objective functions: naive and hybrid.
result The hybrid objective function outperforms the naive one in robustness on noisy input data.
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%.
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.
UDVD uses deep learning to denoise videos without supervision.
problem Lack of clean video data for training deep learning models.
method UDVD is a CNN trained solely on noisy video data, adapting to local motion.
result UDVD performs as well as supervised methods, even with limited training data.
Quantum reservoir computing tackles noisy quantum computers for temporal tasks.
problem Efficiently process input sequences on noisy quantum computers.
method Quantum reservoir computing using dissipative quantum dynamics.
result Small and noisy quantum reservoirs can handle high-order nonlinear temporal tasks.
We study the Gaussian Process regression model in the context of training data with noise in both input and output. The presence of two sources of noise makes the task of learning accurate predictive models extremely challenging. However, in some instances additional constraints may be available that can reduce the unc…
Critical initialisation strategies are identified for noisy ReLU networks.
problem Understanding signal propagation in noisy rectifier neural networks.
method Developed a new framework for signal propagation in stochastic regularized neural networks, incorporating various noise distributions.
result Critical initialisation strategies for multiplicative noise (e.g. dropout) are identified, but not for additive noise.
Simple method improves deep classifier accuracy under noisy labels.
problem Training deep classifiers with noisy labels.
method Probabilistic approach using temperature parameterized softmax.
result Improves accuracy, log-likelihood and calibration on noisy datasets.
New hypothesis and method reduce noise in saliency maps.
problem Noisy saliency maps from deep neural networks.
method Proposed a new hypothesis and Rectified Gradient method.
result Rectified Gradient effectively reduces saliency map noise.
Exact bounds derived for neural network outputs with noisy inputs.
problem Bounding the output distribution of neural networks with random inputs.
method Applying ReLU NNs to derive bounds for general NNs, then using these to find exact error guarantees.
result Exact upper and lower bounds for the output distribution of neural networks with random inputs.
Self-training with noisy student-teacher boosts keyword spotting accuracy.
problem Robust keyword spotting in challenging conditions.
method Aggressive data augmentation and self-training with noisy student-teacher approach.
result Significant accuracy improvement in difficult conditions, up to 60%.
PML-GAN tackles noisy multi-label annotations using adversarial learning.
problem Learning multi-label models from noisy, overcomplete annotations.
method PML-GAN uses a disambiguation network and a generative adversarial network to map noisy labels to clean labels and data samples.
result PML-GAN achieves state-of-the-art performance on partial multi-label learning datasets.
Neurons predict future scalar inputs by learning top modes of lag vectors.
problem Predicting future scalar inputs with physiological delays.
method Normal Mode Decomposition to extract independently evolving modes.
result Temporal filters of neurons correspond to left eigenvectors of a generalized eigenvalue problem.
We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, an…
The paper develops GP classifiers for noisy inputs in multi-class classification.
problem Input noise in real-world data affects supervised learning performance.
method Developed multi-class Gaussian Process classifiers using variational inference.
result The proposed methods outperform noise-ignoring GP classifiers in predictive distribution.
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.
Develops ADMM for estimating precision matrices from noisy, missing data.
problem Estimating precision matrices from noisy and missing data.
method Alternating Direction Method of Multipliers (ADMM) for non-positive semidefinite inputs.
result Empirically compares ADMM with existing methods and characterizes tradeoffs.
Method learns low-dim. state vars from noisy high-dim. data.
problem Discovering dynamical models from noisy high-dimensional data.
method Stochastic Variational Deep Kernel Learning with encoder and latent model.
result Effective denoising, compact state representation, and uncertainty quantification.
This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs us…
A new method combines experts' opinions to train regression models with noisy labels.
problem Training regression models with noisy labels from multiple experts.
method Estimate each labeler's expertise and combine opinions using learned weights.
result Empirically outperforms existing techniques on simulated and real data.
A new type of random forest improves robustness against noisy data.
problem Noise in test samples damages random forest performance.
method Introduces denoising autoencoders into random forests to identify and correct incorrect decisions.
result Improves estimation accuracy by considering multiple traversal paths for incorrect nodes.
In this note, we introduce a new algorithm to deal with finite dimensional clustering with errors in variables. The design of this algorithm is based on recent theoretical advances (see Loustau (2013a,b)) in statistical learning with errors in variables. As the previous mentioned papers, the algorithm mixes different t…
SelectMix improves deep learning robustness against noisy labels.
problem Deep neural networks memorize noisy labels, degrading performance.
method Confidence-guided targeted sample mixing with soft labels.
result SelectMix consistently outperforms baseline methods on noisy label datasets.
This paper examines error bounds for deep learning classifiers with noisy labels.
problem Understanding the performance of classifiers trained on noisy data.
method Derives error bounds for excess risk, decomposing it into statistical and approximation errors. Uses independent block construction for statistical dependencies and vector-valued setting for approximation error.
result Established theoretical results for error bounds in deep learning with noisy labels, mitigating the impact of high-dimensional input spaces.
A new clustering method using deep autoencoder networks and spectral clustering.
problem Improving clustering accuracy in noisy data.
method Dual autoencoder network for robust latent representations, mutual information estimation for discriminative features, deep spectral clustering.
result Significantly outperforms state-of-the-art clustering approaches on benchmark datasets.
This paper considers the problem of subspace clustering under noise. Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise is added to the unlabelled input data points, which are assumed to be in a union of low-dimensional subspaces. We show that a modified vers…
Curriculum learning helps neural networks learn parities more efficiently.
problem Improving learning efficiency for neural networks on parity targets.
method Using a curriculum learning approach with a mixture of sparse and dense inputs.
result A 2-layer ReLU neural network can learn parities more efficiently than a fully connected network.
RFMs transition from linear to nonlinear under specific input-label correlation.
problem Understanding the transition from linear to nonlinear behavior in RFMs.
method Analyzing RFMs under spiked covariance designs, characterizing the interaction between anisotropy and input-label correlation.
result The RFM generalization error is governed by the strength of input-label correlation, leading to a clear nonlinear advantage above a specific boundary.
Algorithm identifies bilinear dynamical systems from noisy data.
problem Learning a realization of a partially observed bilinear dynamical system.
method Regression of outputs to highly correlated covariates for Markov-like parameters.
result High probability error bounds on identification algorithm under uniform stability assumption.
Improves magnetic field mapping using an array of magnetometers with noisy input.
problem Improving magnetic field maps in indoor environments with noisy magnetometer data.
method Uses Gaussian process regression with an array of magnetometers, incorporating known array positions and relative magnetometer locations.
result The method produces higher quality magnetic field maps compared to using a single magnetometer.
Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input. However, these models are vulnerable to adversarial perturbations--small input changes crafted explicitly to fool the model. In this paper, we ask whether a DNN can distinguish adversarial …