However, if the focus is on minimizing the number of parameters and model complexity, the TP-Unet+AE model can be utilized to achieve a smaller parameter count and reduced complexity. Nevertheless, in the context of medical imaging, where accurate segmentation is of utmost importance, prioritizing...
Model complexity is a fundamental problem in deep learning. In this paper, we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity. We review the existing...
These numbers are orders of magnitude larger than those for typical low or intermediate complexity models discussed in the literature, and they pose a new set of practical and conceptual questions. The aim of this work is to give some initial and, at this stage, necessarily tentative answers ...
However, the larger uncertainty in the projection of soil C by the two models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty. In addition, ...
4.1. Reduced model complexity The application of ACM when driven with site-level meteorological data had a consistently high correlation to SPAc GPP estimates for both single-site and multi-site calibrations (Table 4). Therefore, a reduction in model complexity, including temporal resolution (i.e...
Instead, we should strive to strike the balance between model complexity and tractability that maximizes our ability to accurately elucidate evolutionary history. We believe MSC models achieve such a balance, while acknowledging that even more complex models, such as those involving networks, may prove...
[7,9] however have the promise to allow for better grouping. Last but not least, inference time is always a key consideration for pose estimation models. Often, model complexity has to be treated for speed and thus many models do not consider all spatial relations that would be beneficial ...
(LS-SVM). LS-SVM is a generalized scheme for classification and also incurs low computation complexity in comparison with the ordinary SVM scheme [40]. One can find more details about calculating LS-SVM in Appendix B, available in the online supplemental material. The following sections explain...
Our kernel regression theory can be applied separately to each element of the target function vector (Methods), and a generalization error can be calculated by adding the error due to each vector component. We can visualize the complexity of the two tasks by plotting the projection of the data...
A similar division of labor is apparent in the connections between the striatum and frontal cortex, although with added complexity (Gittis et al, 2010). Striatal GABAergic interneurons control the output of the striatum to frontal cortex via the direct and indirect pathways (which express D1 ...