Data-centric AI, with its primary focus on the collection, management, a...
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for
...
This project presents a deep learning approach to generate monophonic
me...
People primarily consult tables to conduct data analysis or answer speci...
Recent studies have found that summaries generated by large language mod...
Large Language Models (LLMs), such as BERT and GPT-based models like Cha...
Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining signif...
Interpretability and efficiency are two important considerations for the...
While the use of graph-structured data in various fields is becoming
inc...
The volume of open-source biomedical data has been essential to the
deve...
The Pretrained Foundation Models (PFMs) are regarded as the foundation f...
The rise of pre-trained unified foundation models breaks down the barrie...
The acquisition of high-quality human annotations through crowdsourcing
...
Despite the recent progress in language generation models, their outputs...
Human evaluation is the foundation upon which the evaluation of both
sum...
Unsupervised graph representation learning (UGRL) has drawn increasing
r...
Currently, attention mechanism becomes a standard fixture in most
state-...
Graph neural networks (GNNs) have shown their superiority in modeling gr...
Despite the successes of neural attention models for natural language
ge...
Unfaithful text generation is a common problem for text generation syste...
Abstractive summarization models are commonly trained using maximum
like...
Despite data's crucial role in machine learning, most existing tools and...
Recent years have witnessed fast developments of graph neural networks (...
Anomaly detection from graph data is an important data mining task in ma...
In recent years, graph neural networks (GNNs) have emerged as a successf...
A classification scheme of a scientific subject gives an overview of its...
Fast-developing fields such as Artificial Intelligence (AI) often outpac...
Anomaly detection from graph data has drawn much attention due to its
pr...
As infamous invaders to the North American ecosystem, the Asian giant ho...
Detecting anomalies for dynamic graphs has drawn increasing attention du...
In this paper, we present a conceptually simple while empirically powerf...
Although some recent works show potential complementarity among differen...
With the rapid development of NLP research, leaderboards have emerged as...
Graph neural networks (GNNs) have emerged as effective approaches for gr...
In most cases, the lack of parallel corpora makes it impossible to direc...
Model compression aims to reduce the redundancy of deep networks to obta...