AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proves that incremental learning still occurs, and introduces a symmetry-based finite-width approximation via symmetrized networks, rather than comparing directly with an infinite-width limit, which yields better control of approximation errors and may be of independent interest.
A new bound on total deviation for time-homogeneous MDPs is proved and a cutting bonus that preserves both optimism and the monotonicity needed for planning is designed that preserves both optimism and the monotonicity needed for planning.
This is the first global convergence and recovery result for EM or Gradient EM beyond the special case of m=2, and it is proved that with only mild over-parameterization, randomly initialized gradient EM converges to the ground truth with polynomial time and samples.
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