Imbalance learning is a subfield of machine learning that focuses on lea...
Machine-learning models are prone to capturing the spurious correlations...
Adversarial training is an effective learning technique to improve the
r...
Sample weighting is widely used in deep learning. A large number of weig...
Recently, graph (network) data is an emerging research area in artificia...
Features, logits, and labels are the three primary data when a sample pa...
As learning difficulty is crucial for machine learning (e.g.,
difficulty...
An effective weighting scheme for training samples is essential for lear...
Weighting strategy prevails in machine learning. For example, a common
a...
A common assumption in machine learning is that samples are independentl...
In recent years, Graph Neural Network (GNN) has bloomly progressed for i...
The knowledge contained in academic literature is interesting to mine.
I...
Literature analysis facilitates researchers to acquire a good understand...
This study refers to a reverse question answering(reverse QA) procedure,...
Literature analysis facilitates researchers better understanding the
dev...
Question answering (QA) is an important natural language processing (NLP...
Though deep neural networks have achieved state-of-the-art performance i...
Sentiment analysis is a key component in various text mining application...
Colors play a particularly important role in both designing and accessin...