LEARNING DEEP REPRESENTATIONS: PLATFORM AND ALGORITHMS

Abstract

DEEP LEARNING HAS RECEIVED UNPRECEDENTED ATTENTION FROM RESEARCHERS OF DIFFERENT AREAS DURING THESE TWO YEARS. IN THIS THESIS, A SERIES OF WORKS ARE INTRODUCED WHICH CONTRIBUTE TO THE FIELD OF DEEP LEARNING IN TWO ASPECTS, NAMELY ?PLATFORM AND ALGORITHMS?. IN THE PLATFORM PART, A DEEP LEARNING PLATFORM CALLED PURINE IS DESIGNED BASED ON BI-GRAPH ABSTRACTION, PURINE ACCELERATE NEURAL NET TRAINING BY HARNESSING THE POWER OF MULTIPLE GPUS ON MULTIPLE MACHINES. IN THE ALGORITHMS PART, WE PROPOSED THE ?NETWORK IN NETWORK?(NIN) AND THE ?NEURAL DYNAMICAL SYSTEM?(NDS) ALGORITHMS. THE NIN ALGORITHM IS BUILT ON THE CONVOLUTIONAL NEURAL NETWORK STRUCTURE. IT ENHANCES THE LOCAL DISCRIMINATIVE POWER OF THE CONVOLUTIONAL NEURAL NETWORK BY REPLACING THE LINEAR CONVOLUTIONAL FILTERS WITH A FEEDFORWARD NEURAL NETWORK. THE NDS ALGORITHM ADDS FEEDBACK LOOPS TO THE FEEDFORWARD NEURAL NETS BASED ON A DIFFERENTIAL NEURON MODEL, WHICH GENERALIZES BOTH FEEDFORWARD NEURAL NETS AND ENERGY BASED MODELS.