3948ead63a9f2944218de038d8934305-AuthorFeedback.pdf

"The authors provided strong reasoning behind why a uniform shape is beneficial"; "The paper is easy to follow"; "Authors did enough experiments on different data sets and different neural networks"; Below we address the main suggestions for improvements. "There is little explanation about the impact of Kurtois to the activation quantization." "...a solution that can easily modify the step size to become a power of two would be very desirable." "Is there any particular reason of choosing Kurtosis over other statistical measure, such as coefficient of variation?" "In table 1, it can be observed that from 4-bit quantization to 3-bit quantization, the performance drops a lot. "No experimental parameter settings are provided, and no comprehensive comparison with the latest SOTA method "I don't get the claim of the title of this paper "One model to rule them all"" -- We store a single set of weights ("one In contrast, we allow for a single model to operate at various quantization levels (e.g., employ a 4-bit variant of the "Second, the comparasion between KURE and the baseline model could be biased in T able 1.