Gaussian process models simplify neural network behavior for easier understanding.
problem Understanding and predicting the behavior of deep learning systems.
method Constructing surrogate models using Gaussian processes from finite neural networks.
result Surrogate models capture phenomena like spectral bias and predict generalization well.
Generalizes NTK for surrogate gradient learning in neural networks.
problem Lack of theoretical foundation for surrogate gradient learning.
method Generalizes neural tangent kernel (NTK) for surrogate gradient learning (SGL).
result Surrogate gradient NTK provides a good characterization of SGL.
Improved MALA method for neural networks uncertainty quantification.
problem Uncertainty quantification in Bayesian neural networks.
method Corrected Stochastic MALA (csMALA) with a simplified correction term.
result Improved surrogate posterior for quantifying uncertainties in neural networks.
The training of stochastic neural network models with binary (±1) weights and activations via continuous surrogate networks is investigated. We derive new surrogates using a novel derivation based on writing the stochastic neural network as a Markov chain. This derivation also encompasses existing variants of the s…
Improved neural network surrogates for ICF using manifold and cycle consistency.
problem Modeling and predicting complex physical processes in inertial confinement fusion.
method Training neural network surrogates that are consistent with the physical manifold and cyclically consistent.
result Surrogates are superior in predictive performance, more resilient to sampling artifacts, and more data efficient.
Study examines neural network surrogate models for uncertainty in heat conduction problems.
problem Capturing the full distribution of solution fields, especially at the tails.
method Comparison of feed-forward fully connected network and Deep Operator Network architectures using data-driven and physics-informed loss functions.
result Worst-case prediction errors are significantly larger than mean field errors, highlighting the importance of extreme samples.
Neural surrogate predicts SPN rates from token trajectories.
problem Challenging parameter estimation in SPNs with covariates.
method 1D Convolutional Residual Network trained on Gillespie-simulated SPN realizations.
result Surrogate predicts rate-function coefficients with RMSE = 0.043.
Develops multi-modal neural network models for improved prediction and uncertainty quantification.
problem Improving prediction accuracy and uncertainty quantification for multi-modal data.
method Multi-modal Bayesian neural network models with conjugate last-layer estimation using SVI.
result Improved prediction accuracy and uncertainty quantification compared to uni-modal models.
Deep neural networks improve surrogate models for non-smooth quantities in uncertain geometries.
problem Building accurate surrogates for non-smooth quantities in uncertain geometries.
method Deep neural networks for point evaluation of solutions to interface problems with geometric uncertainties.
result Neural networks provide good surrogates without suffering from the curse of dimensionality.
Deep neural networks provide meaningful uncertainty estimates for large-scale simulations.
problem Uncertainty estimates for deep neural network predictions from large-scale simulations.
method General variational inference approach to calibrate Bayesian uncertainties.
result Calibrated Bayesian uncertainties preserved physics-correlations in predicted quantities.
ASEs use surrogate estimation to efficiently evaluate model performance with minimal labels.
problem Efficient model evaluation with limited labels.
method Surrogate-based estimation and active learning.
result ASEs offer greater label-efficiency than current methods for deep neural networks.
The performance of deep neural networks crucially depends on good hyperparameter configurations. Bayesian optimization is a powerful framework for optimizing the hyperparameters of DNNs. These methods need sufficient evaluation data to approximate and minimize the validation error function of hyperparameters. However, …
Proposes a method to improve surrogate models by incorporating sensitivity information.
problem Pruned neural networks often fail to capture sensitivities and uncertainties of original models.
method Combines Interval Adjoint Significance Analysis and Sobolev Training to accurately model sensitivities.
result Pruned models based on the proposed method better match original sensitivities.
Paper proposes a method to minimize non-differentiable loss functions.
problem Minimizing non-differentiable and non-decomposable loss functions.
method Learn smooth relaxations of true losses through surrogate neural networks, then optimize jointly with the prediction model.
result Empirical results show the efficiency of learning surrogate losses.
Efficiently quantifies uncertainty in subsurface flow using neural networks guided by theory.
problem Uncertainty in dynamic subsurface flow predictions.
method Theory-guided Neural Network (TgNN) for efficient uncertainty quantification.
result TgNN surrogate improves efficiency of uncertainty quantification compared to MC method.
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) 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.
Study examines adversarial robustness of ANN variants, revealing differences in black-box settings.
problem Adversarial robustness of alternative neural network architectures.
method Analysis of conventional, stochastic ANNs, and SNNs across three datasets; experiments in white-box and black-box settings.
result Stochastic ANNs are more robust than conventional ANNs in black-box settings, especially with surrogate attacks.
Gradient-informed BNNs improve Bayesian optimization performance.
problem Improving Bayesian optimization with gradient information.
method Gradient-informed loss function for Bayesian neural networks.
result Gradient information enhances BNN predictions and BO convergence.
Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classificat…
Study on calibration and consistency of adversarial surrogate losses.
problem Designing robust classifiers with theoretical guarantees.
method Extensive analysis of H-calibration and H-consistency of adversarial surrogate losses.
result Some convex loss functions and supremum-based convex losses are not H-calibrated for important hypothesis sets.
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which faces several challenges given a large number of parameters, complex and multimodal posterio…
Fingerprint deep neural networks by conferrable adversarial examples.
problem Detecting model stealing attacks in machine learning as a service.
method Extract conferrable adversarial examples to fingerprint deep neural networks.
result First method to reach 1.0 ROC AUC in verifying surrogates.
Deep learning model approximates stochastic responses.
problem Approximating stochastic responses using neural networks.
method Generative neural network with conditional maximum mean discrepancy (CMMD) loss.
result Excellent performance on benchmark problems.
This work improves neural likelihood surrogates for stochastic models with a score-augmented loss.
problem Efficient parameter inference for stochastic models with computationally expensive likelihood functions.
method Score-augmented loss function for neural network likelihood surrogates.
result Improves surrogate quality at a lower computational cost compared to generating more data.
New algorithm optimizes PAC-Bayes bound without surrogate loss.
problem Mismatch between optimisation objective and generalisation bound in stochastic neural networks.
method Proposes a novel training algorithm that optimizes the PAC-Bayesian bound directly.
result Empirical results show improved performance over existing PAC-Bayesian training methods.
PFNs4BO uses neural processes for flexible Bayesian Optimization.
problem Efficient surrogate modeling for Bayesian Optimization.
method In-context learning of PFNs to approximate posterior predictive distribution.
result PFNs outperform traditional GP, BNN in BO tasks.
Meta-materials simulation sped up with energy surrogates.
problem Challenging simulation of complex meta-materials due to high-fidelity PDEs.
method Learned component-level surrogates using neural networks to model stored potential energy.
result Surrogates enable accurate macroscopic behavior simulation without full structure simulation.
Bayesian deep learning improves building energy simulation accuracy.
problem Uncertainty in surrogate models for building energy performance.
method Training dropout neural networks and stochastic variational Gaussian Processes.
result Surrogate models reduce errors by up to 30% with uncertainty-aware sampling.
Paper discovers differential equations from data using neural networks and Bayesian methods.
problem Discovering differential equations from datasets using machine learning.
method Integrates neural network-based surrogates with Sparse Bayesian Learning (SBL).
result Proposes a robust model discovery algorithm and a Physics Informed Normalizing Flow (PINF).
Study proposes a differentiable surrogate loss function for optimizing Fβ score in binary classification with imbalanced data.
problem Non-differentiability of Fβ score makes it unsuitable for optimization by gradient-based learning. method Investigated relationship between Fβ score and loss functions, proposed a differentiable surrogate loss function. result Gradient paths of the proposed surrogate Fβ loss function approximate the gradient paths of the Fβ score. Bayesian optimization (BO) is an effective method of finding the global optima of black-box functions. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft sim…
Bayesian optimization uses BNNs as efficient surrogate models for expensive function evaluations.
problem Optimizing expensive objective functions using Gaussian process surrogates.
method Study of Bayesian neural networks (BNNs) as alternatives to standard Gaussian process (GP) surrogates for optimization.
result Infinite-width BNNs are particularly promising, especially in high dimensions.
Study uses neural networks to detect nonlinear dynamics in short time series.
problem Challenges in testing dynamical nonlinearities in short time series.
method Recurrent neural network classification framework using raw time series data.
result Classifier accuracy is higher than 50% for chaotic processes, around 50% for nonlinearly correlated noise.
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such as mass or energy conservation, must be satisfied to obtain reliable results. However, standard machine learning algorithm…
DeepONets improve surrogate modeling for engineering systems.
problem Accurately modeling complex PDEs for engineering systems.
method DeepONets specialize in approximating mathematical operators for PDEs.
result DeepONets achieve high prediction accuracy and zero-shot capability.
New method uses sparse random features for crashworthiness analysis.
problem Efficient surrogate modelling for uncertainty quantification.
method Sparse Random Features combined with self-supervised dimensionality reduction.
result Superiority over state-of-the-art techniques in crashworthiness analysis.
Unified information-theoretic objectives for training deep neural networks.
problem Difficulty in computing information-theoretic quantities for large deep neural networks.
method Review and unify competing objectives, develop surrogate objectives.
result Surrogate objectives allow applying information bottleneck to modern neural network architectures.
Improves inverse uncertainty quantification for time-dependent data using PCA and deep neural networks.
problem Efficiently quantify model input uncertainties from time-dependent experimental data.
method Functional PCA for dimensionality reduction, deep neural networks for surrogate modeling, Bayesian neural networks for uncertainty estimation.
result The proposed method reduces the computational cost and improves the agreement with experimental data.
Bayesian neural networks with functional priors improve surrogate modeling in mechanics.
problem Challenges in integrating prior knowledge and quantifying uncertainties in high-dimensional NN parameter spaces.
method Anchored ensembling to integrate a priori information and learn low-rank correlations between NN parameters.
result Effective transfer of knowledge between function-space and parameter-space priors improves surrogate model accuracy and uncertainty estimation.
Bayesian optimization with preference learning using monotonic neural networks.
problem Optimizing complex systems with multiple conflicting objectives.
method Proposes a neural network ensemble for utility surrogate modeling, leveraging monotonicity.
result Demonstrates superior performance compared to existing methods.
New method improves adversarial transferability from Bayesian neural networks.
problem Improving the effectiveness of black-box evasion attacks.
method Sampling from the posterior distribution of neural network weights to build a surrogate.
result Significantly improved success rates of state-of-the-art attacks (up to 83.2 percentage points).
Fast, reliable, and error-bounded option pricing with neural networks
problem Fast, reliable, and error-bounded option pricing
method Mixture Density Network
result Out-of-sample CDF error of 1.4imes10−4 Graph Neural Simulators improve data efficiency for PDE surrogates.
problem Lack of data efficiency in neural operators for PDE systems.
method Graph Neural Simulators (GNS) leverage message-passing and numerical time-stepping to learn PDE dynamics efficiently.
result GNS achieves less than 1% relative L2 error using only 3% of available trajectories.
ML surrogates speed up Bayesian inverse problem solving.
problem Infer source location from noisy acoustic wave equation data.
method Use neural network as surrogate for PDE, apply MCMC to posterior.
result Accurately infers source location from noisy data.
New method improves consistency in preference learning for neural networks.
problem Inconsistent surrogate losses in preference learning for neural networks.
method Formulated a margin-shifted ranking framework and introduced Structure-Aware H-consistency. result Proved superior consistency guarantees for capacity-bounded models using heavy-tailed surrogates.
DAWN dynamically embeds watermarks in neural network predictions to prevent model extraction.
problem Preventing theft of machine learning models through model extraction.
method Dynamic adversarial watermarking at prediction API level.
result DAWN effectively resists two state-of-the-art model extraction attacks.
We propose a new framework to improve the calibration of neural networks.
problem Improving the accuracy of model confidence predictions.
method Introducing a differentiable surrogate for expected calibration error (DECE) and a meta-learning framework to optimise model hyper-parameters for validation set calibration.
result Achieved competitive performance with existing calibration approaches.
A neural network speeds up bond-associated peridynamics simulations.
problem High computational costs in bond-associated peridynamics.
method Message-passing neural network (MPNN) for bond-associated peridynamic material correspondence formulation.
result Significantly reduces computation time via GPU acceleration.