In recent years, there have been attempts to increase the kernel size of Convolutional
Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers'(ViTs) self …

Nonlinear activation functions are pivotal to the success of deep neural nets, and choosing
the appropriate activation function can significantly affect their performance. Most networks …

Time series analysis has a wide range of applications in the fields of weather forecasting,
traffic management, fault detection, intelligent operation, etc. In the real world, time series …

Sparsity in deep neural networks has been extensively studied to compress and accelerate
models for environments with limited resources. The general approach of pruning aims at …

The exploration of large-scale unknown environments can benefit from the deployment of
multiple robots for collaborative map**. Each robot explores a section of the environment …

Multivariate Time Series Classification (MTSC) is a fundamental data mining task, which is
widely applied in the fields like health care and energy management. However, the existing …

Convolutional Neural Networks (CNNs) are the backbone of many deep learning methods,
but optimizing them remains computationally expensive. To address this, we explore …

Internet of Things and Deep Learning are synergetically and exponentially growing
industrial fields with a massive call for their unification into a common framework called …

Deep-learning accelerators are increasingly in demand; however, their performance is
constrained by the size of the feature map, leading to high bandwidth requirements and …

Time series analysis is a fundamental data mining task that supervised training methods
based on empirical risk minimization have proven their effectiveness on specific tasks and …