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Found 4 papers, 1 papers with code
Efficient Sampling for Predictor-Based Neural Architecture Search
Usually, we compute the proxy for all DNNs in the network search space and pick those that maximize the proxy as candidates for optimization.
Iteratively Training Look-Up Tables for Network Quantization
For each layer, we learn a value dictionary and an assignment matrix to represent the network weights.
Mixed Precision DNNs: All you need is a good parametrization
Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable.
Iteratively Training Look-Up Tables for Network Quantization
In this paper we introduce a training method, called look-up table quantization, LUT-Q, which learns a dictionary and assigns each weight to one of the dictionary's values.
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