A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
Artificial intelligence (AI)—it's the end of the tox as we know it (and I feel fine)
The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has
the potential to transform chemical safety evaluation. Toxicology has evolved from an …
the potential to transform chemical safety evaluation. Toxicology has evolved from an …
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 …
Laplace redux-effortless bayesian deep learning
Bayesian formulations of deep learning have been shown to have compelling theoretical
properties and offer practical functional benefits, such as improved predictive uncertainty …
properties and offer practical functional benefits, such as improved predictive uncertainty …
Do bayesian neural networks need to be fully stochastic?
We investigate the benefit of treating all the parameters in a Bayesian neural network
stochastically and find compelling theoretical and empirical evidence that this standard …
stochastically and find compelling theoretical and empirical evidence that this standard …
Sampling from Gaussian process posteriors using stochastic gradient descent
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential
decision-making but are limited by the requirement of solving linear systems. In general, this …
decision-making but are limited by the requirement of solving linear systems. In general, this …
Adapting the linearised laplace model evidence for modern deep learning
The linearised Laplace method for estimating model uncertainty has received renewed
attention in the Bayesian deep learning community. The method provides reliable error bars …
attention in the Bayesian deep learning community. The method provides reliable error bars …
Bayesian low-rank adaptation for large language models
Parameter-efficient fine-tuning (PEFT) has emerged as a new paradigm for cost-efficient fine-
tuning of large language models (LLMs), with low-rank adaptation (LoRA) being a widely …
tuning of large language models (LLMs), with low-rank adaptation (LoRA) being a widely …
Gflowout: Dropout with generative flow networks
Bayesian inference offers principled tools to tackle many critical problems with modern
neural networks such as poor calibration and generalization, and data inefficiency …
neural networks such as poor calibration and generalization, and data inefficiency …
Position paper: Bayesian deep learning in the age of large-scale ai
In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …
achieving high predictive accuracy in supervised tasks involving large image and language …