Reviews: Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices

The main driving observations include (a) presence of a signature in a small fraction of the data, and (b) that the signatures are predictable using a prefix of the data. The algorithm, EMI-RNN, Early Multi-Instance RNN, shows significant reduction in computation (about 80%) while maintaining/improving the accuracy of the models marginally. Discussion on gains achieved (accuracy, resources used, and time taken) with respect to percentage of noise in the data should help establish the importance of the approach taken by EMI-RNN. This makes reader uncomfortable and causes doubts. Please consider restructuring this part.