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Werestructured the textsuch that the NSM isbetter and earlier introduced inthe main text, add derivation7 details and provide the experimental results. Previously unexplained terms (e.g., the offset parameter to the noise)8 are now elaborated. We have not attempted NSMs in a (deep) RL framework as we are not aware of convincing9 theoretical/practical work on successful deep RL with binary neural networks. Reviewer 2 Abbreviation SNN (Stochastic Neural Network) was changed to StNN. We suggest "Inherent Weight17 Normalization inStochastic Neural Networks" asalessconfusing title.

Neural Information Processing Systems http://nips.cc/

In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives,

This is a static policy which always uses the same attribute until a11 negativereward isencountered (L167), atwhich point anewattribute issampled.

Due to the increasingly large size of datasets and high model complexity, direct minimisation of the regularised problem is posing more and morecomputationalchallenges.

Its explicit estimation of the posterior distribution of the contextfeature covariance leads tosubstantial empirical gains overapproximate approaches.

's are real-valued weights depending on the accuracy of h