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Sine Activated Low-Rank Matrices for Parameter Efficient Learning
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning.
D'OH: Decoder-Only random Hypernetworks for Implicit Neural Representations
We instead present a strategy for the initialization of run-time deep implicit functions for single-instance signals through a Decoder-Only randomly projected Hypernetwork (D'OH).
On Quantizing Implicit Neural Representations
The role of quantization within implicit/coordinate neural networks is still not fully understood.
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