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Found 64 papers, 15 papers with code
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning
We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed losses for downstream tasks.
An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
Optimizing the learning rate remains a critical challenge in machine learning, essential for achieving model stability and efficient convergence.
LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems
It also effectively handles unbounded growth functions and accumulated numerical errors in the Laplace domain, thereby overcoming challenges in the identification process.
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators
This paper proposes a new data-driven methodology for predicting intervals of post-fault voltage trajectories in power systems.
Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach
In this work, we introduce a novel two-level optimization framework, which utilizes the K-Medoids clustering algorithm in conjunction with the Open-Source Routing Machine engine, to optimize food bank and pantry locations based on real road distances to houses and house blocks.
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility
We argue that clean training experiences poor convergence in the off-manifold direction caused by the ill-conditioning in widely used first-order optimizers like gradient descent.
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertainty quantification.
Large Language Model Sentinel: LLM Agent for Adversarial Purification
In this paper, we introduce a novel defense technique named Large LAnguage MOdel Sentinel (LLAMOS), which is designed to enhance the adversarial robustness of LLMs by purifying the adversarial textual examples before feeding them into the target LLM.
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
Replica exchange stochastic gradient Langevin dynamics (reSGLD) is an effective sampler for non-convex learning in large-scale datasets.
Robust Diffusion Models for Adversarial Purification
We propose a novel robust reverse process with adversarial guidance, which is independent of given pre-trained DMs and avoids retraining or fine-tuning the DMs.
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression.
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization
The deep neural networks are known to be vulnerable to well-designed adversarial attacks.
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
Approximate Thompson sampling with Langevin Monte Carlo broadens its reach from Gaussian posterior sampling to encompass more general smooth posteriors.
Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model
Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles.
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions
This work redesigns MIONet, integrating Long Short Term Memory (LSTM) to learn neural operators from time-dependent data.
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas.
A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients
To address this challenge, this paper proposes a deep operator learning-based framework that requires a limited high-fidelity dataset for training.
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems.
Backdiff: a diffusion model for generalized transferable protein backmapping
Despite recent progress in data-driven backmapping approaches, devising a backmapping method that can be universally applied across various CG models and proteins remains unresolved.
An Element-wise RSAV Algorithm for Unconstrained Optimization Problems
We present a novel optimization algorithm, element-wise relaxed scalar auxiliary variable (E-RSAV), that satisfies an unconditional energy dissipation law and exhibits improved alignment between the modified and the original energy.
Energy-Dissipative Evolutionary Deep Operator Neural Networks
For an objective operator G, the Branch net encodes different input functions u at the same number of sensors, and the Trunk net evaluates the output function at any location.
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
Due to the complex behavior arising from non-uniqueness, symmetry, and bifurcations in the solution space, solving inverse problems of nonlinear differential equations (DEs) with multiple solutions is a challenging task.
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
This paper presents NSGA-PINN, a multi-objective optimization framework for effective training of Physics-Informed Neural Networks (PINNs).
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators
This paper designs an Operator Learning framework to approximate the dynamic response of synchronous generators.
Non-reversible Parallel Tempering for Deep Posterior Approximation
Notably, in big data scenarios, we obtain an appealing communication cost $O(P\log P)$ based on the optimal window size.
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems
This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e. g. the power grid or traffic) with an underlying sub-graph structure.
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation
We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost.
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems
In this work, we propose an adaptive sparse learning algorithm that can be applied to learn the physical processes and obtain a sparse representation of the solution given a large snapshot space.
Federated X-Armed Bandit
This work establishes the first framework of federated $\mathcal{X}$-armed bandit, where different clients face heterogeneous local objective functions defined on the same domain and are required to collaboratively figure out the global optimum.
2-d signature of images and texture classification
We introduce a proper notion of 2-dimensional signature for images.
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification
This paper proposes a new dimension reduction framework based on rotated multi-fidelity Gaussian process regression and a Bayesian active learning scheme when the available precise observations are insufficient.
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems
We propose a new multi-resolution autoencoder DeepONet model referred to as MultiAuto-DeepONet to deal with this difficulty with the aid of convolutional autoencoder.
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations
We introduce three different models: continuous time, discrete time and hybrid models.
Federated Online Sparse Decision Making
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters.
Interacting Contour Stochastic Gradient Langevin Dynamics
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions.
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories
This paper proposes a new data-driven method for the reliable prediction of power system post-fault trajectories.
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction
Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers.
On Convergence of Federated Averaging Langevin Dynamics
We develop theoretical guarantees for FA-LD for strongly log-concave distributions with non-i. i. d data and study how the injected noise and the stochastic-gradient noise, the heterogeneity of data, and the varying learning rates affect the convergence.
Deformation Robust Roto-Scale-Translation Equivariant CNNs
Incorporating group symmetry directly into the learning process has proved to be an effective guideline for model design.
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs
To enable DeepONets training with noisy data, we propose using the Bayesian framework of replica-exchange Langevin diffusion.
Non-reversible Parallel Tempering for Uncertainty Approximation in Deep Learning
Parallel tempering (PT), also known as replica exchange, is the go-to workhorse for simulations of multi-modal distributions.
PCNN: A physics-constrained neural network for multiphase flows
To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations.
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks
Deep learning-based surrogate modeling is becoming a promising approach for learning and simulating dynamical systems.
Bayesian data-driven discovery of partial differential equations with variable coefficients
In this work, we propose an advanced Bayesian sparse learning algorithm for PDE discovery with variable coefficients, predominantly when the coefficients are spatially or temporally dependent.
A consistent and conservative model and its scheme for $N$-phase-$M$-component incompressible flows
Numerical tests indicate that the proposed model and scheme are effective and robust to study various challenging multiphase and multicomponent flows.
Computational Physics Numerical Analysis Numerical Analysis Fluid Dynamics
BCGGAN: Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network
Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science.
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm (SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian learning in big data statistics.
Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
Evaluating the mechanical response of fiber-reinforced composites can be extremely time consuming and expensive.
Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study
These results are an important first step in translating the combination of self-sensing materials and EIT to real-world SHM and NDE.
An adaptive Hessian approximated stochastic gradient MCMC method
The bias introduced by stochastic approximation is controllable and can be analyzed theoretically.
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potential of the acceleration.
Augmented Gaussian Random Field: Theory and Computation
We prove that under certain conditions, the observable and its derivatives of any order are governed by a single Gaussian random field, which is the aforementioned AGRF.
Statistics Theory Probability Statistics Theory
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC) algorithms.
Ranked #75 on
Image Classification
on CIFAR-100
(using extra training data)
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression
We compare this method with the conventional multi-fidelity Cokriging method that does not use gradients information, and the result suggests that GE-Cokriging has a better performance in predicting both QoI and its gradients.
Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns
Case studies were performed to classify the images using CNNs and determine the PMB, LBS, and VES models' suitability.
Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires
In material modeling, the calculation speed using the empirical potentials is fast compared to the first principle calculations, but the results are not as accurate as of the first principle calculations.
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors
Rotation symmetry is a general property for most symmetric fluid systems.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.
Ranked #8 on
Click-Through Rate Prediction
on Avazu
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors.
Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media
In particular, for the flow problem, we design a network with convolutional and locally connected layers to perform model reductions.
Numerical Analysis Numerical Analysis
SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations
We demonstrate how to use our algorithm step by step and compare our algorithm with threshold sparse Bayesian regression (TSBR) for the discovery of differential equations.
Bayesian inverse regression for dimension reduction with small datasets
We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $\-x$ and the response variable $y$.
Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation
We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables.
Latent Transformations for Object View Points Synthesis
We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited for real-time applications.
