Study shows how numerical discretization affects reconstructions and parameter distributions in nano metrology.
problem Impact of numerical discretization on parameter reconstructions and model parameter distributions.
method Bayesian target vector optimization, finite element model, Gaussian process, stochastic machine learning surrogate models, Markov chain Monte Carlo sampler.
result Numerical discretization parameters impact the accuracy and distribution of reconstructed model parameters.
Generative Adversarial Networks optimize model parameters for image matching.
problem Optimizing model parameters for accurate image matching.
method Model-Assisted Generative Adversarial Network (GAN) to produce fake images matching true images.
result Best match model parameter values can minimize bias in image recognition.
Study examines the cost of tuning hyper-parameters in regression models.
problem Estimating optimal hyper-parameters for regression models.
method Established finite-sample oracle inequalities for hyper-parameter selection.
result Generalization error of selected models shrinks at nearly a parametric rate.
A new method estimates parameters of complex models using ordinary least squares.
problem Estimating parameters of nonlinear dynamic models from time series data.
method Physics-Informed Regression (PIR) using regularized ordinary least squares.
result PIR outperforms physics-informed neural networks (PINN) in parameter estimation.
A new method transfers parameters in ELM networks using projective model.
problem Parameter transfer in extreme learning machine networks.
method Projective model to bridge source and target model parameters, L2,1-norm penalty for joint feature selection and parameter transfer.
result Significantly outperforms non-transfer ELM networks and other methods.
Deep learning estimates time-varying Markov model parameters.
problem Estimating time-dependent parameters in Markov models.
method Reframes parameter estimation as an optimization problem using maximum likelihood.
result Real solution close to SDE with neural network-derived parameters under specific conditions.
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be v…
ABC with sensitivity analysis improves parameter estimation in biological models.
problem Parameter estimation in systems biology models from noisy data.
method Approximate Bayesian Computation (ABC) coupled with sensitivity analysis.
result Sloppy parameters can be estimated less precisely, while stiff parameters need careful estimation.
Bayesian method estimates model parameters and errors from complex data.
problem Fitting complex models to real data with many parameters and errors.
method Simultaneous analysis of many data sets to overcome degeneracy.
result Can estimate model parameters, model error, and instrument errors.
MPF method improves parameter estimation in probabilistic models.
problem Difficulty in fitting probabilistic models due to intractable partition function.
method Minimum Probability Flow (MPF) method for parameter estimation.
result MPF outperforms existing techniques in convergence time and accuracy.
Deep learning used for parameter estimation in hard-to-infer models.
problem Parameter estimation in intractable models like max-stable processes.
method Train deep neural networks on simulated data to estimate parameters.
result Deep learning provides accurate and faster parameter estimation.
NanoFlow reduces parameter complexity in normalizing flows.
problem Efficient parameter complexity in flow-based models.
method Single neural density estimator with flow indication embedding.
result Sublinear parameter complexity achieved.
Training-free model learns SDE dynamics without training, accelerating parameter studies.
problem High computational cost of simulating parameter-dependent SDEs.
method Training-free conditional diffusion model with joint kernel-weighted Monte Carlo estimator.
result Accurate approximation of conditional distributions across varying parameter values.
Bayesian classification and regression with high order interactions is largely infeasible because Markov chain Monte Carlo (MCMC) would need to be applied with a great many parameters, whose number increases rapidly with the order. In this paper we show how to make it feasible by effectively reducing the number of para…
ABC improves cognitive model parameter estimation from behavioral data.
problem Estimating cognitive model parameters from human behavioral data.
method Approximate Bayesian Computation (ABC) for parameter conditioning.
result ABC improves parameter estimates and supports individual user fitting.
Study identifies Markov chain model parameters from small assortments.
problem Identifying parameters of Markov chain choice models from large assortments.
method Simple and efficient algorithm to recover parameters from assortments of sizes two and three.
result Parameters of the Markov chain choice model can be identified from assortments of sizes two and three.
New method models unknown systems with hidden parameters using neural networks.
problem Modeling unknown dynamical systems with hidden parameters.
method Training a deep neural network (DNN) model using trajectory data of the unknown system.
result DNN model accurately predicts unknown dynamical systems with new initial conditions.
Two approaches improve parameter learning in various mixture models.
problem Parameter learning in mixture models.
method Complex-analytic and algebraic-combinatorial methods.
result Improved sample sufficiency for parameter estimation in specific mixture models.
Paper models buildings' thermal characteristics with a Bayesian approach.
problem Modeling buildings' heat dynamics with various factors.
method Bayesian state-space model incorporating prior knowledge.
result Bayesian approach provides similar parameters as MCMC but faster.
Automates fitting semiconductor device models using approximate Bayesian computation.
problem Manual tuning of parameters for fitting TFT models to experimental data is inefficient and prone to errors.
method Approximate Bayesian Computation (aBc) for generating posterior distributions of estimated parameters.
result The proposed method accurately predicts model parameters from mobility curves using gradient boosted trees.
Online algorithm estimates dynamic regression parameters and their variance.
problem Estimating time-varying parameters in regression models.
method Derive approximate online algorithm for mean and variance estimation using Kalman filter.
result Equivalent to extended Kalman filter for mean and variance estimation.
Adapters add few trainable parameters per task, improving NLP performance.
problem Parameter inefficiency in fine-tuning large pre-trained models for multiple downstream tasks.
method Adapter modules that add only a few trainable parameters per task, allowing for high parameter sharing and task extensibility.
result Adapters achieve near state-of-the-art performance with minimal additional parameters.
New method improves parameter estimation in complex stochastic models.
problem Parameter calibration in stochastic models with unavailable analytical likelihood.
method Gradient-based simulated parameter estimation with multi-time scale stochastic approximation.
result Enhanced estimation accuracy and reduced computational costs.
Image-to-image networks speed up SAR model parameter estimation.
problem Computational infeasibility of MLE for large, non-stationary spatial fields.
method Used image-to-image networks to estimate SAR model parameters.
result Image-to-image networks enable faster and more accurate parameter estimation.
Paper efficiently infers differential parameters in time-varying models using time score matching.
problem Efficiently inferring differential parameters in time-varying probabilistic models.
method Directly estimates the differential parameter using time score matching and proves consistency of the method.
result Consistent estimation of parameter derivatives in high-dimensional settings.
Study models forest transitions with deep learning for parameter estimation.
problem Complex dynamics of forest, agricultural, and abandoned lands.
method Developed a stochastic differential equation model and used deep learning for parameter estimation.
result Deep learning approach estimates model parameters from time-series data.
Single neural network predicts ImageNet model parameters for faster training.
problem Training diverse ImageNet models requires significant resources and time.
method Trained a neural network to predict ImageNet model parameters and used them for initialization.
result Models initialized with predicted parameters converge faster and achieve competitive performance.
Investigates ways to train larger models with fewer resources, finding that test loss depends only on the actual number of trainable parameters.
problem Training larger models for cheaper under hardware constraints.
method Emulates an increase in effective parameters using frozen random parameters or fast structured transforms.
result Scaling laws cannot be deceived by spurious parameters; test loss depends only on the actual number of trainable parameters.
DEKF improves recommender systems by allowing flexible parameter dynamics.
problem Improving online recommender systems with flexible parameter dynamics.
method Specialized decoupled extended Kalman filter (DEKF) for factorization models.
result DEKF enhances flexibility and parameter uncertainty in recommender systems.
A new co-clustering model for high-dimensional data reduces parameter complexity.
problem High-dimensional data challenges traditional co-clustering methods.
method Parameter-wise co-clustering model with SEM and Gibbs sampler for estimation.
result The model maintains parsimony while offering more flexibility.
Develops black-box methods to estimate parameters of complex models.
problem Lack of efficient methods to produce simulations for complex statistical models.
method Pre-training deep neural networks on extensive simulated databases for well-structured likelihoods. Iterative algorithm for other complex dependencies.
result Successfully estimates and quantifies uncertainty of parameters from non-Gaussian models.
Efficient algorithms for sparse parameter recovery in mixture models.
problem Support recovery of high-dimensional sparse latent vectors in mixture models.
method Efficient algorithms with logarithmic sample complexity dependence on dimensionality.
result First guarantees on support recovery for various mixture models.
Study shows best hyper-parameters improve deep learning model's accuracy for IoT attack detection.
problem Improving accuracy of deep learning model for IoT attack detection.
method Examined three hyper-parameters' influence on model performance.
result Model's reported accuracy not achievable due to optimal hyper-parameters.
The paper assesses methods to model parameter uncertainty in reserve risk under Solvency II.
problem Parameter uncertainty impacts reserve risk under Solvency II.
method Comparing standard methods to Solvency II requirements, the paper evaluates and adapts a method to model parameter uncertainty.
result The adapted method yields a risk capital model for reserve risk achieving the required confidence level.
The article reviews how to set stochastic volatility model parameters.
problem Choosing parameters for stochastic volatility models.
method Examines existing literature on various methods.
result Different approaches to setting stochastic volatility parameters.
FNFs model parameter-dependent densities by combining a fixed flow with a polynomial parameter-dependent transformation.
problem Learning a separate flow for every parameter configuration is intractable.
method Factorizable Normalizing Flows (FNFs) represent the parameter-dependent density as a fixed flow for a reference configuration and a learnable polynomial transformation factorized over parameters.
result FNFs enable the recovery of the combined effect of multiple parameters without sampling their joint space, providing a scalable and interpretable solution.
EPD method accurately captures parameter distributions from RCS data.
problem Limitations of traditional methods in estimating parameter distributions from RCS data.
method EPD method generates synthetic trajectories, estimates parameters, and selects parameters based on discrepancy.
result EPD provides accurate distribution of parameters without data loss.
Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that…
Study on measuring vulnerability of neural network parameters via corruption.
problem Understanding the robustness and generalization of deep neural networks.
method Proposes an indicator to measure parameter robustness via parameter corruption and provides a gradient-based estimation.
result Demonstrates the effectiveness of the proposed indicator and training method in improving parameter robustness and accuracy.
Framework synthesizes programs for simulating complex models and estimating parameters.
problem Parameter estimation for complex models requires manual encoding of fixed model structures.
method Combines LLMs for program synthesis with neural simulation-based inference.
result Identifies plausible model families from open-ended prompts with high accuracy.
The Schwartz-Smith model parameters are estimated using Kalman Filter with additional constraints.
problem Estimating parameters of the Schwartz-Smith model for risk-neutral pricing of futures contracts.
method Kalman Filter method with additional constraints to address parameter identification problem.
result The obtained parameter estimates are the conditional Maximum Likelihood Estimators (MLEs) evaluated within the Kalman Filter.
Paper proposes a new approach for stochastic gradient descent in probabilistic modeling.
problem Finding optimal predictions in probabilistic models with large step sizes.
method Averaging moment parameters instead of natural parameters for constant-step-size stochastic gradient descent.
result Constant-step-size SGD can lead to better predictions in some cases and always converges in infinite-dimensional models.
Paper introduces combining model and parameter uncertainty in BNNs.
problem Combining model and parameter uncertainty in scalable BNNs.
method Adapted variational inference with reparametrization for model space constraints.
result Sparsification of BNNs structure via Bayesian model averaging and selection.
A new method distills material models from noisy data without prior selection.
problem Uncertainty in material model discovery from noisy data.
method Augmenting data with Gaussian process, approximating parameter distribution with normalizing flow, distilling by matching stress-deformation functions, performing sensitivity analysis.
result Sparse and interpretable material models discovered from experimental data.
Add expert knowledge to resolve ambiguities in ANN models.
problem Non-unique parameter fitting in material science.
method Augment a black-box ANN model with expert knowledge at two levels.
result Expert knowledge resolves ambiguities in parameter space.
A new method quantizes LSTM gate parameters without performance loss.
problem Quantization loss in LSTM gate parameters without performance degradation.
method Lossy quantization of gate parameters during training, weight parameters adjust to offset quantization loss.
result F1 score decreased by only 0.7% on Named Entity Recognition dataset.
A new method estimates time-varying parameters in earth system models using offline and online data assimilation.
problem Estimating time-varying parameters in complex earth system models.
method Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE-PF)
result HOOPE-PF outperforms existing methods, especially with small ensemble sizes.
Investigates how stochastic volatility models affect European option pricing under parameter uncertainty.
problem How do stochastic volatility models impact European option pricing when parameters are uncertain?
method Formalizes the problem as a control problem, uses dual representation with backward stochastic differential equations, and applies numerical solutions to market data.
result Conservative model-prices cover 98% of market-prices for European call options.