Adaptive Computation Time: Tailoring Neural Network Efficiency for Real-World Applications

Abstract

Context: As neural networks are complex and tasked with processing increasingly diverse and intricate data, the need for efficient computation becomes critical.

Problem: Traditional neural network architectures typically apply a fixed amount of computational effort to all inputs, which can lead to inefficiencies, mainly when dealing with inputs of varying complexity.