Swish
PulseAugur coverage of Swish — every cluster mentioning Swish across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New SG-Blend activation function improves neural network robustness
Researchers have introduced SG-Blend, a novel adaptive activation function designed to improve neural network representations. SG-Blend interpolates between Swish and GELU activation functions, allowing each layer to le…
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AI security startup AIR raises $50M for agent vetting platform
AI security startup AIR has secured $50 million in funding across two seed rounds to develop a platform for monitoring the software supply chain used by AI agents. Founded by former Israeli intelligence officers, AIR ai…
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New structural interpretation of GELU and other activation functions proposed
Researchers have proposed a new structural interpretation of activation functions like GELU, ReLU, SiLU/Swish, and hard swish. This work views GELU not just as a stochastic gate output, but through a Gaussian complement…
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New algorithm offers robust learning for nonlinear AI models
Researchers have developed a novel algorithm for robustly learning Gaussian Single Index Models (SIMs) even when faced with heavy-tailed noise and adversarial corruption. This new method provides the first robust recove…
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Paper analyzes floating-point neural network expressivity
Researchers have published a paper exploring the expressive power of neural networks operating with floating-point arithmetic, moving beyond theoretical models that assume exact real numbers. The study introduces a fram…
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New method secures embedded neural networks against timing attacks
Researchers have developed a new methodology for implementing activation functions in embedded neural networks that prevents information leakage through timing side channels. This approach ensures consistent execution t…
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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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Researchers evolve activation functions to handle missing data in neural networks
Researchers have developed a novel approach called Three-Channel Evolved Activations (3C-EA) to address challenges in machine learning when dealing with missing data. Unlike traditional activation functions, 3C-EA incor…