This work introduces a new metric to assess the fidelity of surrogate models to the underlying data-generating signal.
problem The limitations of fidelity-based explanations in explainable AI.
method Introduces the linearity score λ ( f ) λ(f) λ ( f ) to quantify the extent of a regression network's linear decodability. result High-fidelity surrogates can underperform compared to simpler models and even linear baselines trained directly on the data.
Novel algorithm compresses ECG signals with preserved R peaks.
problem Efficiently compressing ECG signals while preserving R peak information.
method Blaschke unwinding AFD for faster convergence and higher fidelity.
result The proposed algorithm outperforms state-of-the-art approaches in ECG signal compression.
New model resolves signal ambiguities in ill-posed systems.
problem Signal retrieval from indirect measurements with known models.
method Variational generative model that captures signal distribution.
result Retrieves consistent signals with high fidelity.
WaveGrad generates high-fidelity audio using gradient estimation.
problem Generating high-fidelity audio efficiently.
method Conditional model using score matching and diffusion models, iteratively refining a Gaussian white noise signal.
result WaveGrad can generate high-fidelity audio samples using as few as six iterations.
Two new methods improve block-sparse signal recovery from noisy data.
problem Recovering block-sparse signals with unknown partitions.
method LogLOP-l2/l1 and AdaLOP-l2/l1 methods using log-sum penalty and MCP.
result Our methods outperform existing techniques in estimation accuracy.
DDSP integrates signal processing with deep learning for high-fidelity audio synthesis.
problem Efficiently combining signal processing knowledge with deep learning for audio synthesis.
method Integrates classic signal processing elements with deep learning methods.
result High-fidelity audio synthesis without large models or adversarial losses.
Holographic Invariant Storage uses vector architectures to ensure LLM safety at design time.
problem Mitigating context drift in large language models (LLMs) during deployment.
method Introduces Holographic Invariant Storage (HIS) protocol that combines known properties of bipolar Vector Symbolic Architectures into a design-time safety contract.
result Closed-form guarantees for single-signal recovery fidelity, continuous-noise robustness, and multi-signal capacity degradation are provided and validated.
WaiT improves image generation quality by focusing on natural frequency hierarchy.
problem Standard flow matching treats all spatial frequencies equally, ignoring natural frequency hierarchy.
method WaiT uses lossless wavelets to decompose generation into coarse and fine bands, waiting for the signal in high-frequency bands.
result WaiT achieves a pixel-space FID of 1.43 on ImageNet 512x512, reducing sampling compute by up to 50%.
FCOC framework improves financial volatility forecasting.
problem Tackles dual challenges of feature fidelity and model responsiveness in financial volatility forecasting.
method Synergizes fractal feature extraction and dynamic chaotic oscillation processing.
result Demonstrates profound and generalizable impact on S\&P 500 and DJI datasets.
Paper learns predictive ROMs for combustion from high-fidelity simulations.
problem Predicting combustion dynamics from high-fidelity models.
method Combines physics-based model reduction and machine learning.
result ROMs accurately predict combustion dynamics with significant speedup.
Deep Gaussian Processes model functions on DAGs with partially observed data.
problem Reconstructing and inferring from partially observed functions on DAGs with noisy measurements.
method Place priors over functions on DAGs, theoretically study prior-collapse behavior, and offer a structured variational approximation.
result Almost-sure lower bounds on the preservation of input distinctions and interpretability of simulator hierarchies.
Weak diffusion priors can still perform well in inverse problems.
problem Using mismatched or low-fidelity diffusion priors in inverse problems.
method Extensive experiments and theoretical analysis combining Bayesian-consistency theory and local-correlation analysis.
result Weak priors succeed when measurements are highly informative, and they fail in other regimes.
New method recovers sparse signals from nonlinear observations with robust error bounds.
problem Recovering two sparse vectors from nonlinearly mixed observations with limited data.
method Regularization-based framework combining Huberized data fidelity and generalized folded-concave penalties with a proximal alternating algorithm.
result Estimation error bounds of order σ s log ( n ) / m σ\sqrt{s\log(n)/m} σ s log ( n ) / m at every localized stationary point, with oracle rate σ s / m σ\sqrt{s/m} σ s / m under beta-min condition. Big batch SGD improves optimization by growing batch sizes to maintain accurate gradients.
problem Noisy gradients in classical SGD make adaptive stepsize selection and automatic stopping difficult.
method Adaptive batch size growth to maintain a constant signal-to-noise ratio in gradient approximations.
result Similar convergence rates to classical SGD, automated learning rate selection possible.
DiffWave generates high-fidelity audio waveforms efficiently.
problem Conditional and unconditional audio waveform generation.
method Non-autoregressive diffusion model using Markov chain synthesis.
result DiffWave produces high-quality audios in various tasks.
Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system…
New algorithm improves signal recovery from noisy measurements with theoretical guarantees.
problem Recovering signals from noisy measurements in inverse problems.
method Wasserstein-based projections (WP) replacing analytic regularization with data-driven denoising.
result WP approximates true projection with high probability, providing theoretical guarantees.
Proposes a multi-fidelity machine learning strategy integrating low-fidelity deterministic and high-fidelity Bayesian models.
problem Addressing the accuracy-efficiency trade-off in machine learning with scarce high-fidelity data.
method Integrates a non-probabilistic regression model for low-fidelity with a Bayesian model for high-fidelity, trained in a staggered scheme.
result Achieves comparable performance in mean and uncertainty estimation with reduced training time and effective mitigation of overfitting.
Enhances multi-fidelity modeling with DGPs for different input domains.
problem Improving prediction accuracy with multi-fidelity models using different input domains.
method Extends Deep Gaussian Processes (DGPs) to handle different input domains for high and low-fidelity models.
result Demonstrates improved performance on real-world physical problems.
A new BO framework reduces costs by using low-fidelity data.
problem Optimizing expensive experiments with low-fidelity data.
method Developed a multi-fidelity cost-aware Bayesian optimization framework.
result Significantly outperforms state-of-the-art BO methods.
New metrics improve reliability of image generation evaluation.
problem Lack of reliable metrics for evaluating fidelity and diversity in generative models.
method Proposed density and coverage metrics to diagnose fidelity and diversity separately.
result Density and coverage metrics provide more interpretable and reliable signals.
This work improves surrogate models for balancing accuracy and cost in multi-fidelity methods.
problem Balancing accuracy and computational cost in multi-fidelity methods.
method Develops context-aware surrogate models for multi-fidelity importance sampling and Bayesian inverse problems.
result Context-aware surrogate models can lead to runtime speedups of up to one order of magnitude.
This paper reviews Gaussian process-based multi-fidelity techniques for different fidelity relationships.
problem Combining accurate and cheap models for complex system design.
method Gaussian process-based multi-fidelity modeling techniques for varying fidelity relationships.
result Comparison of techniques on analytical and aerospace engineering problems.
We develop a projected Nesterov's proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with Nesterov's momentum acceleration. The objective function that we wish to minimize is the sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a conv…
PHINN: A generative model for rare-event time series using persistent homology
problem Generating rare events in time series
method Flow-matching framework with dynamic Betti curves and persistence landscape loss
result Outperforms statistical and diffusion baselines in topological fidelity and tail coverage
Machine learning combines high- and low-fidelity models for efficient uncertainty quantification and optimization.
problem Efficiently combining high- and low-fidelity models for uncertainty quantification and optimization.
method Machine learning-based multi-fidelity methods for uncertainty quantification and optimization.
result Unified perspective on multi-fidelity priors for optimization.
This paper improves surrogate modeling for noisy data.
problem Uncertainty in high-fidelity models due to noise.
method Comprehensive framework for multi-fidelity surrogate modeling.
result Estimates uncertainty in high-fidelity model predictions.
A new MCMC method combines low and high-fidelity models to reduce computation.
problem Inefficient computation of expensive target densities in scientific applications.
method Pseudo-marginal MCMC approach using a telescoping series of low-fidelity models.
result Asymptotically exact multi-fidelity MCMC algorithms for reduced computational cost.
This paper presents a method to efficiently estimate rare event probabilities using a combination of high and low-fidelity models.
problem Estimating the probability of failure for complex systems using high-fidelity models is expensive and inaccurate for rare events.
method The paper introduces a multi-fidelity surrogate modeling strategy using active learning and subset simulation to merge high and low-fidelity models.
result The method significantly reduces computational cost while maintaining high accuracy in estimating rare event probabilities.
The paper compares multi-fidelity methods for Gaussian process surrogates in physics.
problem Limited availability of data due to expensive simulations.
method Extending non-linear autoregressive methods to multi-fidelity models and incorporating delay terms.
result Multi-fidelity methods generally have smaller prediction error for the same computational cost.
New method uses low-fidelity data to improve neural network predictions.
problem Improving predictive capability of neural networks for parameterized problems.
method Combines POD and shallow neural network; incorporates low-fidelity data features.
result Improves predictive capability of neural network predictions.
Estimates signals from a continuous dictionary with sparse mixtures using optimization.
problem Estimating signals from a continuous dictionary with unknown mixtures and noise.
method Formulates a regularized optimization problem with data fidelity and ( ℓ 1 , L p ) (\ell_1,L^p) ( ℓ 1 , L p ) -penalty. result High probability bounds on prediction error for the Group-Nonlinear-Lasso solution.
Efficiently predicts high-fidelity PDE solutions using multi-fidelity Gaussian processes.
problem Expensive high-fidelity solutions for PDEs on discretized domains.
method Multi-Fidelity High-Order Gaussian Process (MFHoGP) that integrates multi-fidelity examples and scales to large numbers of outputs.
result Significantly reduces the cost of high-fidelity PDE solutions through efficient Gaussian process modeling.
Develops Co_SVR for multi-fidelity modeling combining HF and LF models.
problem Combining high-fidelity and low-fidelity models for efficient design.
method Support vector regression with kernel function and heuristic algorithm.
result Co_SVR outperforms other multi-fidelity surrogate models in prediction accuracy.
FNO model predicts GCS pressure fields with 81% less data, even with limited high-fidelity data.
problem Accurate prediction of complex physical behaviors in large-scale 3D geological carbon storage problems with limited data.
method Multi-fidelity Fourier Neural Operator (FNO) for efficient training with multi-fidelity datasets.
result Multi-fidelity FNO model predicts pressure fields with reasonable accuracy even with limited high-fidelity data.
New method finds failures in high-fidelity simulators with fewer steps.
problem Finding failures in high-fidelity simulators is expensive and impractical.
method Adaptive stress testing with backward algorithm adaptation from low-fidelity to high-fidelity.
result Significantly fewer high-fidelity simulation steps needed to find failures.
Paper optimizes multi-fidelity function with fast learning rates.
problem Optimizing a locally smooth function with limited budget and varying fidelity approximations.
method Kometo algorithm that achieves simple regret rates without knowing function smoothness or fidelity assumptions.
result Kometo algorithm outperforms previous methods empirically.
New methods combine low and high-fidelity data for accurate surrogate modeling.
problem Challenges in surrogate modeling for high-dimensional outputs with limited training data.
method Projection-based multifidelity linear regression methods integrating low-fidelity and high-fidelity data.
result Multifidelity methods achieve up to 12% improvement in median accuracy compared to single-fidelity methods.
Paper calculates optimal use of cheap and expensive data for model accuracy.
problem Optimal design of experiments for variable fidelity data.
method Minimax error analysis for Gaussian process regression.
result Variable fidelity data can improve model accuracy within budget constraints.
Improves Bayesian optimization for multi-fidelity functions.
problem Inefficient estimation of black-box functions due to ignored or oversimplified correlations between fidelities.
method Proposes DNN-MFBO using deep neural networks to capture complex relationships between fidelities.
result Shows significant improvement in optimization performance on synthetic and real-world datasets.
Proposes a method to estimate conditional quantiles using both high-fidelity and low-fidelity data.
problem Difficulty in estimating conditional quantiles with scarce high-fidelity data.
method Two-stage, model-agnostic method using local quantile link and level function estimation.
result The method yields more accurate quantile estimates and tighter prediction intervals.
This work improves surrogate models using low-fidelity data to enhance accuracy and efficiency.
problem Limited training data makes high-fidelity models unreliable.
method Uses low-fidelity data to augment input space and condition high-fidelity models.
result Increased predictive accuracy and reduced computational cost compared to existing methods.
New neural network training method uses bi-fidelity data to reduce errors.
problem Training neural networks with limited high-fidelity data.
method Bi-fidelity ℓ 1 \ell_1 ℓ 1 -regularization strategies. result Bi-fidelity ℓ 1 \ell_1 ℓ 1 -regularization reduces errors by one order of magnitude. RMFGP combines multi-fidelity models for efficient uncertainty quantification.
problem Efficiently infer quantities of interest with limited high-fidelity data.
method Rotated multi-fidelity Gaussian process with dimension reduction and Bayesian active learning.
result RMFGP model improves accuracy and efficiency in high-dimensional problems.
Paper explores using bi-fidelity data to train neural networks for uncertainty quantification.
problem Training neural networks requires large amounts of data, which may not be available for computationally expensive systems.
method Transfer learning techniques using high- and low-fidelity models, including standard transfer and bi-fidelity weighted learning.
result Bi-fidelity transfer learning improves accuracy over standard training approaches.
This paper uses CausalGANs and RL with LLM to predict bond yields.
problem Challenges in financial bond yield forecasting due to data scarcity and market conditions.
method Proposes a novel framework combining CausalGANs, RL, and LLM for synthetic data generation and trading signals.
result Improves forecasting performance over existing methods with low Mean Absolute Error.
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and …
New method optimizes aircraft design with reduced computation using multi-fidelity models.
problem Efficiently solve complex aircraft design problems with limited computational resources.
method Proposes novel multi-fidelity selection strategies that consider both objective and constraint information.
result Shows 86 % 86\% 86% to 200 % 200\% 200% more constraint compliant solutions with a limited budget.