• BSc thesis graded with A (10.0/10.0) at the UPC TelecomBCN school.
  • Advisors: Víctor Campos (BSC) & Xavier Giró-i-Nieto (UPC)

The complexity of solving a problem can differ greatly to the complexity of posing that problem. Building a Neural Network capable of dynamically adapting to the complexity of the inputs would be a great feat for the machine learning community. One of the most promising approaches is Adaptive Computation Time for Recurrent Neural Network (ACT) \parencite{act}. In this thesis, we implement ACT in two of the most used deep learning frameworks, PyTorch and TensorFlow. Both are open source and publicly available. We use this implementations to evaluate the capability of ACT to learn algorithms from examples. We compare ACT with a proposed baseline where each input data sample of the sequence is read a fixed amount of times, learned as a hyperparameter during training. Surprisingly, we do not observe any benefit from ACT when compared with this baseline solution, which opens new and unexpected directions for future research.