The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state- variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influ…
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48 results for “unknown sources”
Paper tackles open set domain adaptation by detecting unknown classes.
problem Adapting to target domains with unknown classes when label spaces partially overlap.
method Instance-level reweighting strategy combined with Extreme Value Theory for unknown class detection.
result Proposed method outperforms state-of-the-art models on conventional datasets.
Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS alg…
Bayesian method mitigates negative transfer in unknown source data.
problem Negative transfer in transfer learning where target performance worsens after source data consideration.
method Proxy-informed robust method for probabilistic transfer learning (PROMPT).
result Negative transfer can be mitigated without prior knowledge of source data.
New method uses PINNs to solve complex PDEs with sparse measurements.
problem Joint estimation of source and parameters in advection-diffusion equations with limited data.
method Weighted adaptive approach based on neural tangent kernel of PINNs.
result Successful estimation of source function, velocity, and diffusion parameters.
Researchers reconstruct simple Riemannian manifolds from boundary wave arrival times.
problem Reconstructing Riemannian manifolds from unknown interior sources and arrival times.
method Discrete metric approximation using labeled Gromov--Hausdorff distance.
result Finite-time approximations converge to the true Riemannian manifold.
This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix. This issue is formulated in a…
The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conduct…
Measuring wave sources uniquely identifies manifold properties.
problem Determining Riemannian manifold structure from wave observations.
method Semilinear wave equation measurements at a single point.
result Topological, differential, and geometric structure can be inferred.
Identification of patterns from discrete data time-series for statistical inference, threat detection, social opinion dynamics, brain activity prediction has received recent momentum. In addition to the huge data size, the associated challenges are, for example, (i) missing data to construct a closed time-varying compl…
Bayesian method improves EEG source localization and estimates skull conductivity.
problem Improving EEG source localization accuracy with unknown skull conductivity.
method Bayesian Approximation Error approach using conditional Gaussian regression, iterative optimization, and physics-informed learning.
result Clear improvements in EEG source localization accuracy and feasible estimates for unknown skull conductivity.
Proposes a transfer learning framework for sparse SIMs without raw source data.
problem Lack of direct access to raw source data and known link functions in transfer learning.
method Source-data-free framework based on SIM, using summary statistics and a multilayer perceptron.
result Consistent improvements over existing approaches in synthetic and real-world data.
Blind Source Separation is a widely used technique to analyze multichannel data. In many real-world applications, its results can be significantly hampered by the presence of unknown outliers. In this paper, a novel algorithm coined rGMCA (robust Generalized Morphological Component Analysis) is introduced to retrieve s…
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle uns…
This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class l…
Deep learning improves forensic matching of casings.
problem Identifying if two casings came from the same firearm.
method Contrastive neural networks trained on casings dataset.
result Contrastive learning achieved better ROC AUC than CMC.
PhICNet combines physics and deep learning for forecasting and source identification in dynamical systems.
problem Forecasting and identifying unobservable external sources in spatio-temporal dynamical systems.
method Physics-Incorporated Convolutional Recurrent Neural Network (PhICNet).
result PhICNet can forecast dynamics and identify sources for relatively long periods.
A new method trains deep neural networks for open set domain adaptation without negative open set difference.
problem Training deep neural networks for open set domain adaptation without negative open set difference.
method Proposes a new upper bound of target-domain risk, including source-domain risk, ε-open set difference (), distributional discrepancy, and constant. Uses gradient descent for source-domain risk and , and adversarial training for distributional discrepancy. Trains DNNs via minimizing the new upper bound.
result Shows state-of-the-art performance on benchmark datasets.
When a feed-forward neural network (FNN) is trained for source ranging in an ocean waveguide, it is difficult evaluating the range accuracy of the FNN on unlabeled test data. A fitting-based early stopping (FEAST) method is introduced to evaluate the range error of the FNN on test data where the distance of source is u…
This paper improves kernel-based regression using transfer learning.
problem Improving generalization performance in kernel-based regression.
method Two-step kernel-based estimator for known transferable sources and novel aggregation algorithm for unknown sources.
result Established statistical properties and validated effectiveness of proposed methods.
Paper tackles moment estimation under covariate shift with a two-stage algorithm.
problem Estimating moments under covariate shift when source and target distributions differ.
method Proposes a two-stage algorithm: first, an optimal estimator for the source distribution; second, likelihood ratio reweighting for calibration.
result Achieves minimax optimal bound for moment estimation.
Paper tackles transfer learning for contextual multi-armed bandits under covariate shift.
problem Nonparametric contextual multi-armed bandits with covariate shift.
method Established minimax rate of convergence, proposed transfer learning algorithm.
result Achieved near-optimal statistical guarantees for learning in target domain.
Improves neural network performance by dynamically adjusting model weights based on source reliability.
problem Training neural networks on data from unreliable sources leads to poor performance.
method Dynamic re-weighting strategy using likelihood tempering to adjust model weights based on estimated source reliability.
result Significant improvement in model performance when trained on mixtures of reliable and unreliable data sources.
Proposes LVGP for multi-source data fusion in science and engineering.
problem Differences in quality and comprehensiveness of data sources.
method Latent Variable Gaussian Process (LVGP) framework.
result Improved predictions for sparse-data problems.
Blind source separation (BSS), i.e., the decoupling of unknown signals that have been mixed in an unknown way, has been a topic of great interest in the signal processing community for the last decade, covering a wide range of applications in such diverse fields as digital communications, pattern recognition, biomedica…
This paper presents a novel theoretical study of the general problem of multiple source adaptation using the notion of Renyi divergence. Our results build on our previous work [12], but significantly broaden the scope of that work in several directions. We extend previous multiple source loss guarantees based on distri…
Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks. However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments. In this …
Paper proposes a transfer learning method for improving matrix completion.
problem Improving estimation of a low-rank target matrix using auxiliary data.
method Transfer learning procedure leveraging prior information on favorable source datasets.
result Method outperforms traditional methods when source datasets are close to the target matrix.
SATL adapts to varying smoothness in hypothesis transfer learning.
problem Fixed kernel regularization fails in varying smoothness settings.
method Proposes SATL, a two-phase KRR algorithm with adaptive Gaussian kernels.
result SATL achieves minimax optimality with matching upper and lower bounds.
New method disentangles sources of different timescales in planetary seismic data.
problem Unsupervised source separation of multi-scale seismic data from planetary missions.
method Wavelet scattering spectra for multi-scale clustering and variational autoencoder for source separation.
result Disentangles sources with different timescales in InSight mission seismic data.
OpenViewer tackles multi-view learning challenges with interpretability and generalization.
problem Lack of interpretability and insufficient generalization in multi-view learning models.
method OpenViewer introduces a Pseudo-Unknown Sample Generation Mechanism, Expression-Enhanced Deep Unfolding Network, and Perception-Augmented Open-Set Training Regime.
result OpenViewer effectively addresses openness challenges and enhances recognition performance for both known and unknown samples.
Improved linear regression for diverse data batches.
problem Learning from multiple heterogeneous data sources.
method Gradient-based algorithm for different input distributions.
result Significant reduction in the number of required batches and sample size.
Independent component analysis (ICA) is the most popular method for blind source separation (BSS) with a diverse set of applications, such as biomedical signal processing, video and image analysis, and communications. Maximum likelihood (ML), an optimal theoretical framework for ICA, requires knowledge of the true unde…
New algorithm identifies best target arm with known additive relationship between source and target MAB instances.
problem Identifying the best arm in a target MAB instance when only source arms can be pulled and there's a known additive relationship between the two.
method Proposes an LUCB-style algorithm to identify an ε-optimal target arm with high probability.
result Theoretical analysis highlights aspects of the transfer learning problem and recovers the LUCB algorithm for single domain BAI as a special case.
DUE framework models unknown equations from data using deep learning.
problem Unknown equations in complex systems.
method Data-driven modeling using deep learning techniques.
result Framework capable of learning various types of unknown equations.
Principal component analysis (PCA) is a popular method for projecting data onto uncorrelated components in lower dimension, although the optimal number of components is not specified. Likewise, multiple signal classification (MUSIC) algorithm is a popular PCA-based method for estimating directions of arrival (DOAs) of …
New algorithm reduces age of information in wireless networks with unknown channel reliability.
problem Learning optimal source-channel pairs to minimize age of information in wireless networks.
method Introduces AoI regret, novel learning algorithm with bounded AoI regret.
result Developed a learning algorithm with AoI regret, improving upon .
Framework for domain adaptation using pseudo-labels from unlabeled data.
problem Improving prediction accuracy in target domain with covariate shift.
method Kernel GLMs with labeled and pseudo-labeled data, using imputation model for target data.
result Non-asymptotic excess-risk bounds for effective labeled sample size.
Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problem is exacerbated when we rely unrealistically on a single training data source, which is often insufficient to cover the intrinsically complex face variatio…
In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity between the characteristics of the variation of the label functions on the…
Limbo is an open-source C++11 library for Bayesian optimization which is designed to be both highly flexible and very fast. It can be used to optimize functions for which the gradient is unknown, evaluations are expensive, and runtime cost matters (e.g., on embedded systems or robots). Benchmarks on standard functions …
Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to in…
Method learns feature map between source and target domains for high-dimensional regression with missing features.
problem High-dimensional regression with differing feature sets in target and source domains.
method First learns a feature map between missing and observed features using source data, then imputes missing features in target domain, and performs two-step transfer learning for penalized regression.
result Developed upper bounds on estimation and prediction errors for HTL, showing dependence on model complexity, sample size, feature map quality, and domain differences.
End-to-end analysis of SGD for STL with adaptive sub-sampling.
problem Designing SGD for STL with statistical guarantees without prior knowledge of source quality.
method Mixed-sample SGD procedure that alternates between source and target data, maintaining transfer guarantees.
result Mixed-sample SGD converges to a target-adaptive solution with rate.
A Selection of Giant Radio Sources from NVSSastro-ph.GA
Results of the application of pattern recognition techniques to the problem of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are presented and issues affecting the process are explored. Decision-tree pattern recognition software was applied to training set source pairs developed from known NVS…
The paper tackles sampling biases by ensuring minority groups are adequately represented in training data.
problem Sampling biases in training data lead to algorithmic biases in machine learning systems.
method The paper presents adaptive sampling methods to determine if it's possible to assemble a representative dataset from given data sources.
result The methods presented can determine with high confidence if a representative dataset can be assembled from given data sources.
Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separat…
The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ,…