The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …

In the financial domain, risk modeling and profit generation heavily rely on the sophisticated
and intricate stock movement prediction task. Stock forecasting is complex, given the …

Stock price prediction is crucial but also challenging in any trading system in stock markets.
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …

The availability of large amounts of data from continuous glucose monitoring (CGM),
together with the latest advances in deep learning techniques, have opened the door to a …

While state-of-the-art NLP models have been achieving the excellent performance of a wide
range of tasks in recent years, important questions are being raised about their robustness …

The recent dominance of machine learning-based natural language processing methods
has fostered the culture of overemphasizing model accuracies rather than studying the …

We present a novel rationale-centric framework with human-in-the-loop--Rationales-centric
Double-robustness Learning (RDL)--to boost model out-of-distribution performance in few …

Corporate mergers and acquisitions (M&A) account for billions of dollars of investment
globally every year, and offer an interesting and challenging domain for artificial intelligence …

Existing surveys on stock market prediction often focus on traditional machine learning
methods instead of deep learning methods. This motivates us to provide a structured and …