GPINN with Neural Tangent Kernel Technique for Nonlinear Two Point Boundary Value Problems Navnit Jha Ekansh Mallik Neural Processing Letters (2024) A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit R. Pacelli S. Ariosto P. Rotondo Nature Machi...
When adding a group of negative control DAGs to the groups described in FigureS5, the Bayesian scoring procedure correctly did not assign that group to any of the CpGs. This finding supports the ability of the applied Bayesian network model selection procedure to identify plausible dependency struct...
Compare the advantages and disadvantages of eager classification (e.g., decision tree, Bayesian, neural network) versus lazy classification (e.g., k-nearest neighbor, case-based reasoning). Perform a hierarchical clustering of the following one-dimensional set of points: 1, 4, 9, 16, 25, 36...
InterpretML In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be light. InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can...
For control, we performed model selection using an alternative method, the Bayesian Information Criterion (Mazzucato et al., 2019), obtaining comparable results (not shown). To gain further insight into the structure of the model selection algorithm, we performed a post hoc comparison between the ...
Bayesian Model Selection revealed that the model most likely to explain the data included modulation of all the connections between PPC and BA18 and within-BA18 connectivity including only the region that represent the current target location (family 1, model 2: highlighted with dotted-line). PPC...
@incollection{NIPS2017_7062, title = {A Unified Approach to Interpreting Model Predictions}, author = {Lundberg, Scott M and Lee, Su-In}, booktitle = {Advances in Neural Information Processing Systems 30}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. ...
Deep neural networks (DNNs) models have the potential to provide new insights in the study of cognitive processes, such as human decision making, due to their high capacity and data-driven design. While these models may be able to go beyond theory-driven models in predicting human behaviour, ...
Bayesian structural equation modelling (BSEM), we explored the factors explaining cognitive status in the group of older adults. Additionally, we applied transcranial alternating current stimulation (tACS) to a parieto-central network found to underlie visuotactile interactions and working memory matching...
False rumors (often termed “fake news”) on social media pose a significant threat to modern societies. However, potential reasons for the widespread diffusion of false rumors have been underexplored. In this work, we analyze whether sentiment words, as