@misc{spconv2022, title={Spconv: Spatially Sparse Convolution Library}, author={Spconv Contributors}, howpublished = {\url{https://github.com/traveller59/spconv}}, year={2022} } Contributers EvernightAurora: add ampere feature. Note The work is done when the author is an employee at Tusi...
Spatial Sparse Convolution Library. Contribute to nagisa-koyama/spconv development by creating an account on GitHub.
This is a spatially sparse convolution library likeSparseConvNetbut faster and easy to read. This library provide sparse convolution/transposed, submanifold convolution, inverse convolution and sparse maxpool. 2020-5-2, we add ConcatTable, JoinTable, AddTable, and Identity function to build ResNet...
try to increase half performance 5年前 README.md remove windows 10 install part 5年前 format_all.sh format code with clang-format, better c++ code 5年前 setup.py 1.2.1: 4x faster subm indice generation 5年前 README Apache-2.0 简介 Spatial Sparse Convolution in PyTorch...
The results show that SpaGraphCCI achieved the best performance on five datasets; it was slightly inferior to DeepLinc on the HDST platform mouse olfactory bulb dataset, which may be due to the sparse data. Clarify was unable to successfully reconstruct cell–cell communication in the HDST ...
Library size l n We model the library size as the observed total gene counts for each cell: $${l}_{n}=\sum _{g}{x}_{{ng}}$$ Topic proportion z nk We model the topic proportion as a logistic-normal prior that we softmax over the topic dimension to ensure that the topic pro...
2D-SCHAR employs graph convolution networks to process graph-structured human action skeleton data comprising three parts: depth estimation, spatial transformation, and action recognition. The initial two components, which infer 3D information from 2D human skeleton actions and generate spatial ...
However, the observed distribution of barcode abundance is not perfectly uniform in our library, which means that cells are more likely to be labeled with some barcodes than with others. The expected number47,55 of non-uniquely labeled cells, E(X), is then given by: ...
To address the above issues, the MFFCESSC-SSD, a novel single-shot detector (SSD) with multi-scale feature fusion and context-enhanced spatial sparse convolution, is proposed in this paper. First, a global context-enhanced group normalization (CE-GN) layer is developed to address the issue ...
Sparse unmixing, as one of the typical semi-supervised spectral unmixing methods, reformulates the linear spectral unmixing problem as selecting endmembers from a standard spectral library using sparse regression [8]. Since the research into sparse unmixing has progressed, a number of sparse unmixing...