Elu
PulseAugur coverage of Elu — every cluster mentioning Elu across labs, papers, and developer communities, ranked by signal.
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Neural networks achieve super-fast convergence and represent complex functions with floating-point arithmetic
Two new arXiv papers explore theoretical aspects of neural network convergence and representation capabilities. The first paper demonstrates that neural network classifiers can achieve super-fast convergence rates under…
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New research explores activation functions beyond ReLU in neural networks
A new paper explores the theoretical underpinnings of neural network kernels, specifically focusing on activation functions beyond the standard ReLU. Researchers characterized the Reproducing Kernel Hilbert Spaces (RKHS…
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New theory shows compact datasets can be made linearly separable by DNNs
Researchers have developed a theory for relocating compact sets in $\mathbb{R}^n$ to arbitrary target domains using diffeomorphisms. This work demonstrates that such collections can be embedded into $\mathbb{R}^{n+1}$ t…