arXiv research

A new method called MCLMC avoids dissipation in sampling from canonical distributions.

We study approximations of non-Gaussian stationary processes having long range correlations with microcanonical models. These models are conditioned by the empirical value of an energy vector, evaluated on a single realization. Asymptotic properties of maximum entropy microcanonical and macrocanonical processes and the…

A new method for sampling high-dimensional distributions overcomes overfitting.

A principled approach to characterize the hidden structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into group…

Wavelet scattering spectra model non-Gaussian time-series, proving scale invariance for self-similar processes.

New probabilistic constructions for Kähler-Einstein metrics.

Ray tracing sampler improves neural network sampling efficiency and resilience.

Study on convergence of exponential probability measures with applications to maximum entropy models and SGLD.

Multi-headed ensembles boost model performance with faster training.

Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the ensemble prediction is more uniformly distributed. Greater diversity is highly correlated with the increase in ensemble accuracy. Another attr…

Transforms ensemble predictions to maintain interpretability.

Overparameterized ensembles don't offer generalization benefits over single large models.

New method creates diverse neural ensembles for better uncertainty estimation and robustness.

New method certifies joint adversarial robustness of model ensembles.

Embedded ensembles improve neural network performance efficiently.

A framework preserves uncertainty in ensemble distillation.

Study pitfalls of deep learning ensembles in uncertainty estimation.

New hyperparameter ensembles boost neural network performance and uncertainty.

ECV method optimizes ensemble parameters for randomized ensembles.

The paper explores dynamic ensembles for multi-step forecasting.

Bayesian deep ensembles improve prediction accuracy in various settings.

RCAM-based ensemble combines binary classifiers using similarity and vote scheme.

Heterogeneous ensembles built from the predictions of a wide variety and large number of diverse base predictors represent a potent approach to building predictive models for problems where the ideal base/individual predictor may not be obvious. Ensemble selection is an especially promising approach here, not only for …

High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.

New method reduces ensemble size for linear bandits, achieving near optimal regret.

Jointly tuning ensemble models improves performance and uncertainty calibration.

Proposes a new method for deep ensembles that improves accuracy and calibration.

Paper analyzes uncertainty metrics in ensemble learning for healthcare AI.

Self-paced ensemble learning improves audio classification models.

We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…

Hydra distills ensemble models into a single model while preserving diversity and uncertainty.

A fast method estimates stability of ensemble feature selectors.

Ensemble++ uses shared-factor ensembles to scale Thompson Sampling for linear and nonlinear bandits.

Ensemble unsupervised anomaly detection using IRT for hidden ground truth.

New method improves ensemble quality by exploring the pre-train basin more effectively.

Ensembles of deep neural networks significantly improve generalization accuracy. However, training neural network ensembles requires a large amount of computational resources and time. State-of-the-art approaches either train all networks from scratch leading to prohibitive training cost that allows only very small ens…

Efficient neural network ensembles improve image classification reliability and uncertainty quantification.

Paper analyzes ensemble Kalman updates for effective dimension and localization.

Ensembling DNNs improves minority group performance, leading to fairness.

Repulsive deep ensembles improve diversity and Bayesian inference.

This research tackles uncertainty in gradient boosting models using ensemble methods.

Fibonacci Ensembles use Fibonacci weights to improve ensemble learning, inspired by natural growth patterns.

Ensembling improves performance when classifiers disagree more than average.

Ensembles of models often yield improvements in system performance. These ensemble approaches have also been empirically shown to yield robust measures of uncertainty, and are capable of distinguishing between different \emph{forms} of uncertainty. However, ensembles come at a computational and memory cost which may be…

Ens-CGP synthesizes ensemble-based inference with Gaussian processes.

Ensembles improve classifier performance by reducing bias, not variance.

Ensemble models refer to methods that combine a typically large number of classifiers into a compound prediction. The output of an ensemble method is the result of fitting a base-learning algorithm to a given data set, and obtaining diverse answers by reweighting the observations or by resampling them using a given pro…

Ensemble of diverse CNNs detects and mitigates adversarial attacks.