Deep Neural Networks have become commercially viable in fields like machine vision, speech/language processing, data acquisition among other applications. Convolutional Neural Networks (CNNs), Recurrent Neural Network (RNNs) and their variants in some conditions have achieved performance better than human experts. However, existing deep network models are incompatible with low power devices or mission-critical applications due to either high computational & latency cost or memory storage, which makes them unfit to scale. Moreover, less effort has been put in making the architectural improvements modular or model-agnostic. In the developing regions of the world, efficient and frugal learning frameworks will have a huge socio-economic impact. AI can be a game-changer by enabling unique strategies to facilitate social good through domain-experts if ML-prototyping is intuitive. Thus, this paper serves a dual purpose, first is to present easily implementable structural modifications, and, second is to provide a comparative overview of prevalent compression techniques. Finally, we conclude this paper discussing and proposing possible challenges in these areas.

Smarter Prototyping for Neural Learning

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments is a significant issue in this field. For this reason, over the past few years, deep learning models applied to automated skin cancer detection has become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take.

NeurIPS 2019 Workshop Retrospectives Submissions

Smarter Prototyping for Neural Learning

Recent advances in deep learning applied for skin cancer detection