4 research outputs found
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning
Deep Neural Network (DNN) models are usually trained sequentially from one
layer to another, which causes forward, backward and update locking's problems,
leading to poor performance in terms of training time. The existing parallel
strategies to mitigate these problems provide suboptimal runtime performance.
In this work, we have proposed a novel layer-wise partitioning and merging,
forward and backward pass parallel framework to provide better training
performance. The novelty of the proposed work consists of 1) a layer-wise
partition and merging model which can minimise communication overhead between
devices without the memory cost of existing strategies during the training
process; 2) a forward pass and backward pass parallelisation and optimisation
to address the update locking problem and minimise the total training cost. The
experimental evaluation on real use cases shows that the proposed method
outperforms the state-of-the-art approaches in terms of training speed; and
achieves almost linear speedup without compromising the accuracy performance of
the non-parallel approach
Social media mental health analysis framework through applied computational approaches
Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div