[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Hands-on Bayesian neural networks—A tutorial for deep learning users
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …
challenging problems. However, since deep learning methods operate as black boxes, the …
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …
combine data with mathematical laws in physics and engineering in a profound way …
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov - Advances in neural information …, 2020 - proceedings.neurips.cc
The key distinguishing property of a Bayesian approach is marginalization, rather than using
a single setting of weights. Bayesian marginalization can particularly improve the accuracy …
a single setting of weights. Bayesian marginalization can particularly improve the accuracy …
Conformal time-series forecasting
K Stankeviciute, AM Alaa… - Advances in neural …, 2021 - proceedings.neurips.cc
Current approaches for multi-horizon time series forecasting using recurrent neural networks
(RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical …
(RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Bayesian deep ensembles via the neural tangent kernel
B He, B Lakshminarayanan… - Advances in neural …, 2020 - proceedings.neurips.cc
We explore the link between deep ensembles and Gaussian processes (GPs) through the
lens of the Neural Tangent Kernel (NTK): a recent development in understanding the …
lens of the Neural Tangent Kernel (NTK): a recent development in understanding the …
Evaluating scalable uncertainty estimation methods for deep learning-based molecular property prediction
Advances in deep neural network (DNN)-based molecular property prediction have recently
led to the development of models of remarkable accuracy and generalization ability, with …
led to the development of models of remarkable accuracy and generalization ability, with …
A survey on learning to reject
Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …
know), which is an essential factor for humans to become smarter. Although machine …