of scPair enable it to outperform existing methods on multimodal data analysis tasks such as cell state mapping and feature prediction, as well as simultaneous trajectory inference in both RNA and ATAC data components to identify time point-specific feature activity during cellular differentiation. ...
This function is the one on which automatic differentiation is applied to perform parameter learning. The display shows a simple 2-layer neural network g_{\theta}: z \mapsto x, but this applies to any generative model. Tune Hyperparameters Influence of the hyperparameters on the manifold of ...
Much larger time steps can be used without encountering any numerical instability problems, even for systems characterized by stiff differential equations. The algorithms have been tested on a number of sample systems.doi:10.1016/0142-0615(88)90013-0S.C. Tripathy...
Our key in- sight is that depth gradients can be derived analytically us- ing the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our sin...
Geometric deep learning models, like Convolutional Neural Networks (CNNs), show promise as surrogate models for predicting sheet stamping manufacturability but lack design variables essential for inverse problems like geometric optimisation. Recent developments in deep learning have enabled geometry generation...
{reg}\)equal to 1 resulted in the improvement of the deformation field. The method based on image fusion achieved slightly better performance than the standard SIREN model. Sample results for this approach are presented in Fig.7. The weight distribution maps shows the automatic utilization of ...
normals can only be obtained through a differentiation step (Guo et al.,2022; Ben-Shabat et al.,2022) or by defaulting to more complex hybrid representations (Sommer et al.,2022; Ma et al.,2022; Yenamandra et al.,2022; Zobeidi & Atanasov,2021; Yang et al.,2023). We extensively ev...
across blocks within the first 10 trials or so, and differentiation in learning trajectories across blocks appears to begin to plateau after 50 trials or so. Thus, although that research is unpublished, a block length of 50 trials was hit upon and appears to have served well in the interim....
The fast marching method depends on numerical differentiation, which is done on a grid. Even though Sethian points out that the fast marching method can be improved by using finite differences of higher order, this does not necessarily enhance the convergence order of the method. In fact, the ...