Sourav Bhattacharya

TL;DR: It is indicated that on-device sensor and sensor handling heterogeneities impair HAR performances significantly and a novel clustering-based mitigation technique suitable for large-scale deployment of HAR is proposed, where heterogeneity of devices and their usage scenarios are intrinsic.

TL;DR: Experiments show, DeepX can allow even large-scale deep learning models to execute efficently on modern mobile processors and significantly outperform existing solutions, such as cloud-based offloading.

TL;DR: This paper proposes SparseSep, a new approach that leverages the sparsification of fully connected layers and separation of convolutional kernels to reduce the resource requirements of popular deep learning algorithms, and allows large-scale DNNs and CNNs to run efficiently on mobile and embedded hardware with only minimal impact on inference accuracy.

TL;DR: This paper studies the benefits of adopting deep learning algorithms for interpreting user activity and context as captured by multi-sensor systems under wearable data by evaluating four variations of deep neural networks based either on fully-connected Deep Neural Networks (DNNs) or Convolutional Neural networks (CNNs).

TL;DR: The aim of this investigation is to begin to build knowledge of the performance characteristics, resource requirements and the execution bottlenecks for deep learning models when being used to recognize categories of behavior and context.