CAESAR source finder improves automated source extraction for ASKAP surveys.
problem Automated source extraction challenges in ASKAP surveys.
method Extended CAESAR source finder for compact and extended sources.
result Improved algorithm performances and scalability for future ASKAP surveys.
Developing a visual platform for faster astronomical source cataloging.
problem Speeding up cataloging of large area surveys in radio astronomy.
method Integration of advanced source finding and classification tools into a visual analytic platform.
result Improvement and acceleration of cataloging process in astronomical surveys.
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being -- to treat the problem as…
ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.
problem Characterizing radio sources in the Galactic plane using ASKAP observations.
method 912 MHz observations with 15 ASKAP antennas, source characterization using CAESAR.
result Differential source counts in agreement with previous surveys, spectral index estimation for sources.
We explore the application of automated reasoning techniques to unknot detection, a classical problem of computational topology. We adopt a two-pronged experimental approach, using a theorem prover to try to establish a positive result (i.e. that a knot is the unknot), whilst simultaneously using a model finder to try …
Bayesian method finds voids in galaxy surveys with deep neural networks.
problem Finding genuine matter underdensities in sparse galaxy surveys is underconstrained.
method Deep graph neural network evolves 'test particles' to sample from stochastic void definitions.
result Trained model performs well and finds Bayes-optimal void mappings.
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exp…
ξ-torch simplifies physics-informed learning by providing differentiable functionals.
problem Training physics-informed deep neural networks requires differentiable physical simulations.
method ξ-torch offers a library of differentiable functionals for scientific simulations.
result Improves numerical stability and reduces memory requirements for higher order derivatives.
Improved robust regression algorithms with faster runtime and better estimation rates.
problem Statistical regression problems under strong contamination model.
method Nearly-linear time algorithms using robust gradient descent and Sever framework.
result Improved estimation rates and runtime compared to state-of-the-art.
VillageNet clusters high-dimensional datasets without needing cluster count, using K-Means and WLCF.
problem Clustering large, high-dimensional datasets with diverse variables.
method VillageNet operates in two phases: K-Means for village division and WLCF for network community detection.
result VillageNet autonomously determines optimal number of clusters and outperforms other methods in NMI.
The large-scale structure of the universe is comprised of virialized blob-like clusters, linear filaments, sheet-like walls and huge near empty three-dimensional voids. Characterizing the large scale universe is essential to our understanding of the formation and evolution of galaxies. The density range of clusters, wa…
Proposes MDDA for multi-source domain adaptation.
problem Performance decay in deep neural networks due to domain shift between labeled and unlabeled data.
method Multi-source distilling domain adaptation (MDDA) network considering multiple source distributions and target similarities.
result Significantly outperforms state-of-the-art approaches on public DA benchmarks.
StrTransformer recovers sources without labels by optimizing latent matrices and enforcing structural constraints.
problem Unsupervised blind source recovery in signal processing.
method Source-wise structured Transformer framework with latent source matrix optimization, structural regularization, and branch-specific weights.
result StrTransformer learns distinct temporal-scale structures and recovers source-aligned latent trajectories.
This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on lo…
StrADiff separates sources from mixtures without labels, using structured priors.
problem Blind source separation of linear and nonlinear mixtures without labeled data.
method Structured Source-Wise Adaptive Diffusion Framework with Gaussian process priors.
result StrADiff can recover latent source trajectories in an unsupervised manner, especially stable in linear mixtures.
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.
Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.
problem Overoptimistic cross-validation estimates for new patient sources.
method Empirical evaluation of K-fold and leave-source-out cross-validation methods.
result Leave-source-out cross-validation provides more reliable performance estimates.
TASFAR adapts regression models without labeled source data.
problem Lack of labeled source data for domain adaptation.
method Uses prediction confidence to estimate target label distribution and calibrate source model.
result Substantially reduces errors in various regression tasks.
Training a source model optimally for its own task is suboptimal for downstream transfer.
problem The optimality of a source model for its own task hinders downstream transfer performance.
method Analyzes L2-SP ridge regression, characterizes transfer-optimal source penalty, and identifies alignment-dependent effects.
result Transfer benefits from stronger source regularization when aligned imperfectly, and from weaker regularization when aligned perfectly.
This paper proposes an alternative algorithm for multichannel variational autoencoder (MVAE), a recently proposed multichannel source separation approach. While MVAE is notable in its impressive source separation performance, the convergence-guaranteed optimization algorithm and that it allows us to estimate source-cla…
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.
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.
AR-Flow VAE improves blind source separation with flexible autoregressive priors.
problem Unsupervised blind source separation of latent signals from mixtures.
method AR-Flow VAE uses autoregressive flows to model latent sources, enhancing flexibility and capturing complex dependencies.
result AR-Flow VAE effectively separates latent sources, demonstrating improved performance over conventional methods.
Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoenc…
Paper proposes a new method to aggregate multiple sources with different label distributions.
problem Aggregating from multiple target-shifted sources with different label distributions.
method Unified framework to select relevant sources for domain adaptation with limited label, unsupervised, and label partial unsupervised scenarios.
result Empirical results significantly outperform baselines.
SAHMM-VAE separates sources adaptively using hidden Markov priors.
problem Unsupervised blind source separation.
method Source-wise adaptive Hidden Markov prior variational autoencoder.
result Different latent dimensions align with different source-specific temporal organizations.
A new method aligns source and target distributions by tuning their weights.
problem Domain adaptation on unlabeled target datasets using labeled source datasets.
method Weighted Joint Distribution Optimal Transport (WJDOT) method that finds alignment between source and target distributions and re-weighting of source distributions.
result Achieves state-of-the-art performance on simulated and real-life datasets.
New WildMix dataset and Spectro-Temporal Transformer model for better monoaural audio source separation.
problem Challenging monoaural audio source separation.
method Introducing WildMix dataset and Spectro-Temporal Transformer model (STT) with Spectro-Temporal Encoder (STE).
result STT outperforms previous baselines on the WildMix dataset.
New method restores source features for SFDA without source data.
problem Domain adaptation without access to source data.
method Feature Restoration (FR) and Bottom-Up Feature Restoration (BUFR).
result BUFR outperforms existing SFDA methods in accuracy, calibration, and data efficiency.
Paper tackles multi-source domain adaptation for regression.
problem Predicting HDL cholesterol levels using gut microbiome data.
method Two-step procedure: 1) Extend a flexible single-source DA algorithm for classification to regression. 2) Augment with ensemble learning for multi-source DA.
result Consistent improvement in HDL cholesterol level prediction performance over existing methods.
Framework reuses pre-trained models for data-free transfer learning.
problem Challenges in retrieving source data for model training.
method Model Recycling Framework for parameter-efficient training.
result Makes multi-source data-free supervised transfer learning possible.
Optimizes black-box functions with varying costs across multiple sources.
problem Optimizing black-box functions with varying costs across multiple sources.
method Uses Augmented Gaussian Process and Gaussian Process to model fidelity and location-dependent costs, respectively. Uses Confidence Bound acquisition function to select sources and locations.
result The approach significantly outperforms existing methods on Hyperparameters Optimization tasks.
New framework tackles multi-source domain adaptation with optimism and consistency.
problem Adjusting mixture distribution weights and ensuring low error on target domain.
method Mildly optimistic objective function and consistency regularization.
result Beats current state of the art in multi-source domain adaptation.
Improves understanding of PWS by calculating influence of sources and data.
problem Understanding the influence of each component in PWS.
method Proposes source-aware Influence Function (IF) to decompose and calculate influence.
result Improves end model's generalization performance and identifies mislabeling.
This paper motivates and develops source traces for temporal difference (TD) learning in the tabular setting. Source traces are like eligibility traces, but model potential histories rather than immediate ones. This allows TD errors to be propagated to potential causal states and leads to faster generalization. Source …
For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that…
We extend a recent synchronization analysis of exact finite-state sources to nonexact sources for which synchronization occurs only asymptotically. Although the proof methods are quite different, the primary results remain the same. We find that an observer's average uncertainty in the source state vanishes exponential…
Novel unsupervised audio source separation using generative priors.
problem Challenges of supervised methods in audio source separation.
method Generative priors trained on individual sources using projected gradient descent optimization.
result Effective recovery of constituent sources with good-quality estimates.
Adaptive kernel approach learns causal effects from diverse data sources.
problem Learning causal effects from multiple, decentralized data sources in a federated setting.
method Adaptive transfer algorithm using Random Fourier Features to estimate similarities and disentangle loss function components.
result Empirically outperforms baselines on decentralized data sources with different distributions.
Study characterizes harmful low-fidelity data sources for surrogate models.
problem Identifying which low-fidelity data sources to use in constructing surrogate models.
method Employed benchmark filtering techniques to assess harmful sources using limited data.
result Provided guidelines for using low-fidelity sources in an industrial setting.
New ICA method for sources with mixed spectra.
problem Inaccurate separation of sources with temporal autocorrelations and mixed spectra.
method Estimates spectral density functions and line spectra using cubic splines and indicator functions, then maximizes the Whittle likelihood function.
result Outperforms existing ICA methods in simulations and EEG data applications.
A new method for blind source separation using hierarchical structure and KL divergence.
problem Blind source separation of complex interacting signals.
method Hierarchical log-linear model with KL divergence minimization.
result Superior performance compared to existing techniques on images and time series data.
Algorithm identifies sources in product distributions with improved complexity.
problem Identifying sources in mixtures of product distributions.
method Approximate multilinear moments input, 2^{O(k^2)} n^{O(k)} operations.
result First explicit bound on computational complexity of source identification.
Bayesian method improves EEG source imaging accuracy by estimating skull conductivity.
problem Sensitivity of EEG source imaging to skull conductivity modeling.
method Bayesian uncertainty modeling to estimate skull conductivity and focal sources.
result Clear improvements in source localization accuracy and feasible skull conductivity estimates.
Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality i…
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
Paper tackles entity matching over multi-source data, optimizing alignment and mitigating negative transfer.
problem Learning effective entity matching models over multi-source large-scale data with relaxed assumptions.
method Proposes a Relaxed Multi-source Large-scale Entity-matching (RMLE) problem and Incentive Compatible Pareto Alignment (ICPA) method.
result Optimized cross-source alignments and mitigated negative transfer, improving entity matching accuracy.
Develops large-sample theory for non-stationary source separation.
problem Lack of large-sample results for non-stationary source separation methods.
method Large-sample theory for NSS-JD method under specific assumptions.
result Consistency of unmixing estimator and its convergence to Gaussian distribution.