The Organiser:

Faculty of Computer Science and Information Technology, UPM

Universiti Putra Malaysia



Prof. Dr. Sung-Bae Cho

Prospects and Challenges of Deep Learning

Recently, deep learning opens another renaissance of artificial intelligence that is a long dream of human-beings. There are four representative models for deep learning, and two of them can be convolutional neural networks and recurrent neural networks. In this talk, I will give the general idea of both methods, and discuss about the prospects and challenges. Especially, to work out realistic problems, we need a hybrid architecture of several deep learning models. I will also present a generative model via an adversarial process gets a great attention due to the amazing demonstration of performance. It can simultaneously train a generative model to capture the data distribution, and discriminative model to estimate the probability that a sample came from the training data. Experiments with the malware dataset from the Kaggle Microsoft malware classification challenge ( show that the tGAN achieves 95.74% average classification accuracy which is higher than accuracy of other state-of-the-art methods and increases the learning stability


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