Predicting the accuracy of a neural network prior to training

Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. What if you could forecast the accuracy of the neural network earlier thanks to accumulated experience and approximation? This was the goal of a recent project at IBM Research and the result is TAPAS or Train-less Accuracy Predictor for Architecture Search (click for demo). Its trick is that it can estimate, in fractions of a second, classification performance for unseen input datasets, without training for both image and text classification.

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