Learning to Continually Learn with the Bayesian Principle 2024 ICML A Probabilistic Framework for Modular Continual Learning 2023 Arxiv Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference 2022 ICLR Continual Learning via Sequential Function-Space Variational Inference...
We upper bound the target risk by a trade-off between only two terms: The voters' joint errors on the source domain, and the voters' disagreement on the target one. Hence, this new study is simpler than other analyses that usually rely on three terms. We also derive a PAC-Bayesian ...
[19766星][3m] [Jupyter Notebook] camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ...
In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selec
This study is designed to develop clinical outcome assessments across the group of disorders. Methods/design The primary goal of this study is to evaluate the utility of a set of outcome measures on a wide range of LGMD phenotypes and ability levels to determine if it would be possible to ...
PAC-Bayes bound 其中,P\sim\mathcal{N}^{}_{}(0,{\sigma}^{2}_{})表示先验概率,\delta的预测误差被这个bound约束的概率,也就是说有1 - \delta的概率,模型的预测误差满足这个约束。重写之后,把公式中的「Expected sharpness」类比到上述公式10中的max的部分(由于loss取对数之后就变成了相减),可以发现AT...
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming. This has triggered a serious deb
Note that in cases like weighted majority voting rule where a bias is used for the aggregated opinion, the error probability function is also a function of weights [49]. If the weights, in turn, depend on the unknown qualities of the experts then for our algorithm to work, the error ...
Diversification is one of the major components of investment decision-making under risk or uncertainty. However, paradoxically, as the 2007–2009 fina
(Bayesian Standard Ellipse Area (SEAB) mode: 5.49 with 95% credibility interval 5.00–5.82), and 3.72 for males (SEABmode: 3.83 with 95% credibility interval 3.50–3.96) (Fig.4a). Isotopic niches were distinct between the sexes, as male and female SEAs only overlapped by 1.2% (1.1% ...