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A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay
Theoretical framework bounds generalization for gradient descent with weight decay, deriving conditions that explain delayed generalization and grokking.
The paper proves convergence of gradient descent with weight decay to a neighborhood of global minimizers of the empirical l2 loss for a broad class of neural networks. It decomposes population error into data, optimization, and prediction variation errors, deriving cellwise and layerwise approximate-homogeneity bounds on prediction variation along the training trajectory. The resulting necessary and sufficient conditions explain layerwise generalization differences and provide a theoretical characterization of grokking.
Cortex XDR™ Detects New Phishing Campaign Installing NetSupport Manager RAT
Cortex XDR threat hunters uncovered a phishing campaign delivering the NetSupport Manager RAT via a fake password-protected NortonLifelock Word document.
Unit 42 identified a January 2020 phishing campaign using a Microsoft Word document disguised as a password-protected NortonLifelock file. Enabling macros triggered an obfuscated command that built alpaca.bat in the temp directory, which used msiexec to download an MSI payload from quickwaysignstx.com, filtered on the Windows Installer user-agent string. The payload installed a PowerShell script and the campaign, which has delivered NetSupport Manager RAT since at least 2018, showed related activity dating back to early November 2019.