Continuous growth in software, hardware and internet technology has enabled the growth of
internet-based sensor tools that provide physical world observations and data …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Current end-to-end machine reading and question answering (Q\&A) models are primarily
based on recurrent neural networks (RNNs) with attention. Despite their success, these …

In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …

With the rise of the Internet, there is a growing need to build intelligent systems that are
capable of efficiently dealing with early risk detection (ERD) problems on social media, such …

Can machines trace human knowledge like humans? Knowledge tracing (KT) is a
fundamental task in a wide range of applications in education, such as massive open online …

For machine reading comprehension, the capacity of effectively modeling the linguistic
knowledge from the detail-riddled and lengthy passages and getting ride of the noises is …

Recently, the focus on sentiment analysis has been domain dependent even though the
expressions used by the public are unsophisticatedly familiar regardless of the topics or …

Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence
modeling tasks. However, training RNNs on long sequences often face challenges like slow …