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.
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arXiv research
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 “noisy maps”
Study corrects misaligned cadaster maps using noisy supervision.
problem Correcting misaligned cadaster maps with noisy supervision data.
method Iterative training rounds to refine ground truth annotations.
result Reduces noise in cadaster map alignment datasets.
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.
Contagion maps detect network structure in noisy data.
problem Detecting underlying manifold structure in noisy data.
method Using activation times in threshold contagions to map network nodes to high-dimensional space.
result Contagion maps reliably detect manifold structure in noisy data, while Isomap fails.
New method models PDEs from noisy, limited data.
problem Modeling PDEs with incomplete, noisy data.
method Learned linear transformation of spatial grid points, followed by dynamics learning in a reduced basis, then back transformation.
result Rapid high-resolution simulations with smaller training data sets.
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instancesstat.ML
New LP method recovers MAP solution from noisy stable instances.
problem MAP inference on noisy stable instances.
method Designing an algorithm to find nearby perturbation stable instances and using LP relaxation.
result LP approximately recovers the MAP solution from noisy stable instances.
Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regressionstat.ML
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.
A new method learns FCMs from noisy data quickly and robustly.
problem Learning large-scale FCMs with noisy data is time-consuming and lacks robustness.
method Transformed learning problem to convex optimization with least-squares and maximum entropy terms.
result The method is rapid, robust, and performs well on synthetic and real-world datasets.
Enhances speech quality in noisy environments using symbolic sequential modeling.
problem Improving speech quality in noisy conditions.
method Incorporates symbolic sequential modeling into speech enhancement framework.
result Significant improvement in speech quality metrics (PESQ, STOI) on TIMIT dataset.
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.
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.
Paper tackles noisy and expensive likelihoods in complex models.
problem Calibrating parameters in complex models with noisy and expensive evaluations.
method Ensemble Kalman methods and Langevin-based methods for inverse problems.
result Ensemble Kalman methods perform well in noisy conditions, while Langevin methods are adversely affected.
Improved saliency maps for deep neural networks with reduced noise.
problem Noisy explanations in Integrated Gradients for deep neural networks.
method SmoothTaylor, adaptive noising, and SmoothGrad techniques.
result SmoothTaylor and adaptive noising generate better quality saliency maps.
Map matching of GPS trajectories from a sequence of noisy observations serves the purpose of recovering the original routes in a road network. In this work in progress, we attempt to share our experience of feature construction in a spatial database by reporting our ongoing experiment of feature extrac-tion in Conditio…
Empirical Transfer Operators and Finite-Sample Change Detection for Noisy Expanding Interval Mapsstat.ML
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
DDPMs are robust to noisy score estimates and achieve optimal convergence rates in Wasserstein-2 distance.
problem Evaluating the quality of DDPMs in Wasserstein distance with noisy score estimates.
method Established finite-sample guarantees in Wasserstein-2 distance for DDPMs, considering noisy score estimates.
result Optimal convergence rates in Wasserstein-2 distance for DDPMs, matching Gaussian case.
Supervised learning from noisy observations: Combining machine-learning techniques with data assimilationphysics.data-an
Combines machine learning and data assimilation for improved forecasting.
problem Improving forecast accuracy with noisy observations.
method Sequentially learns a machine-learning model using an ensemble Kalman filter.
result The combined model achieves good forecast skill and computational efficiency.
Paper proposes a robust deep graph-based classifier for noisy labels.
problem Difficulty in feature learning with noisy training labels.
method Convolutional neural networks with graph Laplacian regularization (GLR).
result Proposed method outperforms state-of-the-art classifiers on noisy datasets.
Robust image translation model for noisy labels.
problem Learning mappings among multiple domains with noisy labeled data.
method Proposes a novel loss function and techniques to handle noisy labeled data.
result Demonstrates robustness in various settings including synthetic and real-world noise.
Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuitystat.ML
New bounds for mixing time and privacy in projected Langevin algorithm and noisy SGD.
problem Analyzing mixing times and privacy in projected Langevin algorithm and noisy SGD.
method New bounds derived using PABI framework and optimization problems.
result New bounds for mixing time and privacy in projected Langevin algorithm and noisy SGD, showing dependency on gradient regularity.
Bayesian approach learns linear operators from noisy data.
problem Learning linear operators from noisy data in infinite-dimensional spaces.
method Bayesian approach with Gaussian priors.
result Establishes posterior contraction rates and generalization error guarantees.
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.
Theory and method for reducing prediction variance in noisy feature-subsampled ridge ensembles.
problem Reduction of prediction variance in noisy data with feature bagging.
method Developed analytical learning curves for noisy ridge ensembles, introduced heterogeneous feature ensembling.
result Subsampling shifts the double-descent peak, leading to improved performance over a single linear predictor.
Analyzes DNNs trained with noisy gradients, finding FWCs negligible for large n.
problem Analyzing DNNs trained with noisy gradients.
method Introduced analytical framework to analyze non-Gaussian stochastic process.
result FWCs negligible for large n, improving CNN performance.
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little r…
Map matching of the GPS trajectory serves the purpose of recovering the original route on a road network from a sequence of noisy GPS observations. It is a fundamental technique to many Location Based Services. However, map matching of a low sampling rate on urban road network is still a challenging task. In this paper…
Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffe…
Introduces MFVDM for high-dimensional data analysis.
problem Non-linear dimensionality reduction of high-dimensional datasets.
method Combines multiple unitary irreducible representations for nonlinear embeddings.
result Achieves better nearest neighbor search and alignment estimation on noisy data.
Improving speech system performance in noisy environments remains a challenging task, and speech enhancement (SE) is one of the effective techniques to solve the problem. Motivated by the promising results of generative adversarial networks (GANs) in a variety of image processing tasks, we explore the potential of cond…
A novel conLSH algorithm improves alignment of noisy SMRT reads.
problem High error probability in SMRT sequencing data.
method Context-based Locality Sensitive Hashing (conLSH) for efficient alignment.
result Comprehensive improvement in speed and memory requirements compared to rHAT.
Kernel-spectral embedding learns low-dim. structures from noisy data.
problem Learning low-dimensional nonlinear structures from high-dimensional noisy data.
method Adaptive bandwidth spectral embedding using integral operators.
result Convergence to noiseless embeddings and eigenfunctions of integral operators.
Proposes a method to improve weakly supervised image segmentation using deep geodesic priors.
problem Limited availability of high-quality annotations for image segmentation, especially in medical data.
method Integrates a deep geodesic prior extracted from an auto-encoder to reduce the adverse effects of weak labels in segmentation accuracy.
result The proposed method significantly improves segmentation accuracy, boosting performance by 4.4% in dice score for clean labels and up to 6.3% for noisy labels (L2).
Develops goodness-of-fit tests for noisy submanifold samples.
problem Testing non-linear models on noisy submanifold samples.
method Non-linear least-square problem solution and distribution application.
result Residual follows distribution with parameters related to model order and dimension.
Simplifies solving noisy SDPs for low rank matrix recovery problems.
problem Solving SDPs with noisy data for low rank matrix recovery problems.
method Identifies conditions called simplicity to limit error in noisy SDP solutions.
result Simple SDPs can be efficiently solved and their approximate solutions trusted.
Measures time-delay embedding for noisy, sparse data.
problem Applying Takens' embedding theorem to real-world, noisy data.
method Formulated a measure-theoretic generalization of the embedding theorem, using optimal transport.
result Reconstructed full state of dynamical systems from time-lagged partial observations robust to noise and sparsity.
Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noisestat.ML
Proposes GRAB-MDM for robust multiview data fusion.
problem Limited theoretical guarantees for multiview fusion methods in noisy high-dimensional data.
method Generalized Robust Adaptive-Bandwidth Multiview Diffusion Maps (GRAB-MDM) with adaptive bandwidth selection.
result Adaptive bandwidths lead to robust recovery of shared intrinsic structure in noisy multiview data.
Kernel method embeds noisy datasets, capturing shared structures.
problem Limited power in capturing nonlinear structures, noisiness, high-dimensionality, and interpretability issues.
method Kernel spectral joint embeddings using duo-landmark integral operators.
result Consistent recovery of low-dimensional noiseless signals and convergence to eigenfunctions of integral operators.
Reconstruction of a function from noisy data is often formulated as a regularized optimization problem over an infinite-dimensional reproducing kernel Hilbert space (RKHS). The solution describes the observed data and has a small RKHS norm. When the data fit is measured using a quadratic loss, this estimator has a know…
Bayesian model infers strengths from noisy tennis match outcomes.
problem Ranking tennis players from match outcomes.
method Bayesian approach to infer unobserved strengths and mapping function.
result Bayesian approach robust to different model specifications.
Nearly Minimax-Optimal Rates for Noisy Sparse Phase Retrieval via Early-Stopped Mirror Descenteess.SP
Paper studies early-stopped mirror descent for noisy sparse phase retrieval.
problem Recovering a sparse signal from noisy quadratic measurements.
method Early-stopped mirror descent with hyperbolic entropy mirror map.
result Achieves nearly minimax-optimal rate of convergence for -sparse signals.
We characterize learnability for stochastic noisy bandits, identifying optimal query complexities.
problem Learnability of stochastic noisy bandit models.
method Complete characterization through model class analysis and proof of optimal query complexities.
result Characterization of learnability for stochastic noisy bandit models.
Paper presents an efficient algorithm for estimating Lipschitz functions from noisy data.
problem Estimating unknown Lipschitz functions from noisy observations.
method Extends max-affine methods to Lipschitz setting using nonlinear feature expansion and adaptive partitioning.
result Achieves minimax convergence rate with respect to intrinsic dimension, up to logarithmic factors.
Mapping Informal Settlements in Developing Countries with Multi-resolution, Multi-spectral Datacs.LG
Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both gove…
The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints by computing circular colour map induced weights. Fuzzy damping coefficients are obtained for each nucleus or center p…
TAD efficiently finds optimal settings for advanced manufacturing.
problem Optimizing high-dimensional process control parameters for optimal design features.
method TAD uses Gaussian process surrogate models and optimizes log-predictive likelihood to find optimal settings.
result TAD efficiently locates optimal settings with quantified uncertainty.
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.
Kinetic Compressive Sensingphysics.med-ph
Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting. We propose a method, which we name…
In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging. Incorporating image mapping convolutional neural networks (CNN) into iterative image recovery has great potential to resolve this issue. This pape…