Search Results for author:
Found 7 papers, 4 papers with code
Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection
This serves as a reminder to carefully consider sensitivity and confidence in the pursuit of model fairness.
Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being.
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Deep learning's performance has been extensively recognized recently.
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction
In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework.
Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent.
