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Variable rate neural compression for sparse detector data
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DOI：10.1016/j.patter.2025.101452.png)
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Abstract
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• Sparse-voxel key-point selection enables adaptive compression by signal density • Outstanding reconstruction accuracy at higher compression ratios • Lightweight model with just 382 trainable parameters • Compression throughput scales favorably for very sparse data
Keywords:
deep learning
autoencoder
high-throughput inference
data compression
sparse data
sparse neural network
high-energy and nuclear physics
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