A review and comparative study on probabilistic object detection in autonomous driving
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …
recent years, deep learning has become the de-facto approach for object detection, and …
A simple baseline for bayesian uncertainty in deep learning
Abstract We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose
approach for uncertainty representation and calibration in deep learning. Stochastic Weight …
approach for uncertainty representation and calibration in deep learning. Stochastic Weight …
Recent advances in deep learning theory
Deep learning is usually described as an experiment-driven field under continuous criticizes
of lacking theoretical foundations. This problem has been partially fixed by a large volume of …
of lacking theoretical foundations. This problem has been partially fixed by a large volume of …
Statistics of robust optimization: A generalized empirical likelihood approach
We study statistical inference and distributionally robust solution methods for stochastic
optimization problems, focusing on confidence intervals for optimal values and solutions that …
optimization problems, focusing on confidence intervals for optimal values and solutions that …
Bridging the gap between constant step size stochastic gradient descent and markov chains
Bridging the gap between constant step size stochastic gradient descent and Markov chains
Page 1 The Annals of Statistics 2020, Vol. 48, No. 3, 1348–1382 https://doi.org/10.1214/19-AOS1850 …
Page 1 The Annals of Statistics 2020, Vol. 48, No. 3, 1348–1382 https://doi.org/10.1214/19-AOS1850 …
Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints
We study the generalization errors of\emph {non-convex} regularized ERM procedures
using Stochastic Gradient Langevin Dynamics (SGLD). Two theories are proposed with non …
using Stochastic Gradient Langevin Dynamics (SGLD). Two theories are proposed with non …
The implicit regularization of stochastic gradient flow for least squares
We study the implicit regularization of mini-batch stochastic gradient descent, when applied
to the fundamental problem of least squares regression. We leverage a continuous-time …
to the fundamental problem of least squares regression. We leverage a continuous-time …
Vectorial surrogate modeling approach for multi-failure correlated probabilistic evaluation of turbine rotor
For complex structures like aeroengine turbine rotor, its reliability performance is jointly
determined by multiple correlated failure modes. Probabilistic evaluation is an effective way …
determined by multiple correlated failure modes. Probabilistic evaluation is an effective way …
On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration
We undertake a precise study of the asymptotic and non-asymptotic properties of stochastic
approximation procedures with Polyak-Ruppert averaging for solving a linear system $\bar …
approximation procedures with Polyak-Ruppert averaging for solving a linear system $\bar …
NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease
Coronary artery disease (CAD) is one of the main causes of cardiac death around the world.
Due to its significant impact on the society, early and accurate detection of CAD is essential …
Due to its significant impact on the society, early and accurate detection of CAD is essential …