Introducing TAPAS

Forecasting the performance of a deep neural network is a nightmare for every data scientist. Every month, dozens of new deep learning research algorithms are published making incredible claims about their performance. However, applying those algorithms to real world problems requires a leap of faith that the model can achieve similar levels of performance with unseen datasets. Not surprisingly, many of the research algorithms that performed incredibly well for specific datasets miserably fail when apply to different domains as a clear manifestation of the famous "No Free Lunch Theorem". Very recently, researchers from IBM's artificial intelligence(AI) lab in Zurich published a new paper proposing a method that uses neural networks to predict the performance of a new model prior to training.