Google’s ATLAS study reveals how languages help each other in AI training, offering scaling laws and pairing insights for better multilingual models.
Abstract: Efficient representation of sparse matrices is critical for reducing memory usage and improving performance in hardware-accelerated computing systems. This letter presents memory-efficient ...
The issue is not present in standard pre-built LibTorch package. We had to compile Libtorch 2.8.0 from source, since CUDA support for sparse CSR tensor is in beta and not part of official distribution ...
The minimal reproducible code is described below. Consider a standard autocast training framework, where a weight matrix is a learnable parameter stored in float type; and input is a sparse_csr ...
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Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.
A new technical paper titled “Signal processing architecture for a trustworthy 77GHz MIMO Radar” was published by researchers at Fraunhofer FHR, Ruhr University Bochum, and Wavesense Dresden GmbH.
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