Logs play a crucial role in system monitoring and debugging by recording...
Open Information Extraction (OIE) task aims at extracting structured fac...
For example, in machine translation tasks, to achieve bidirectional
tran...
Imitation learning has achieved great success in many sequential
decisio...
Large language models (LLMs) have significantly advanced the field of na...
Large language models (LLMs) have notably enhanced the fluency and diver...
The task of root cause analysis (RCA) is to identify the root causes of
...
Jointly extracting entity pairs and their relations is challenging when
...
The recent trend towards Personalized Federated Learning (PFL) has garne...
Various contrastive learning approaches have been proposed in recent yea...
It has been demonstrated that prompt tuning is highly effective in
effic...
Text summarization has been a crucial problem in natural language proces...
Knowledge-enhanced neural machine reasoning has garnered significant
att...
In this paper, we propose REASON, a novel framework that enables the
aut...
Analogical reasoning is the process of discovering and mapping
correspon...
Personalized Federated Learning (PFL) which collaboratively trains a
fed...
Despite the fact that many anomaly detection approaches have been develo...
Sound Event Early Detection (SEED) is an essential task in recognizing t...
In this paper, we propose an ordered time series classification framewor...
During the past several years, a surge of multi-lingual Pre-trained Lang...
Motivated by the success of pre-trained language models such as BERT in ...
We target the task of cross-lingual Machine Reading Comprehension (MRC) ...
Various graph contrastive learning models have been proposed to improve ...
Compliments and concerns in reviews are valuable for understanding users...
Recent multilingual pre-trained language models have achieved remarkable...
Detecting abnormal activities in real-world surveillance videos is an
im...
Time-series representation learning is a fundamental task for time-serie...
We present FACESEC, a framework for fine-grained robustness evaluation o...
We present a contrasting learning approach with data augmentation techni...
Forecasting on sparse multivariate time series (MTS) aims to model the
p...
Graph Neural Networks (GNNs) have shown to be powerful tools for graph
a...
Despite recent progress in Graph Neural Networks (GNNs), explaining
pred...
Accurate air turbulence forecasting can help airlines avoid hazardous
tu...
Pre-trained language models such as BERT have achieved great success in ...
Discrete event sequences are ubiquitous, such as an ordered event series...
Many users implicitly assume that software can only be exploited after i...
Outlier detection is an important data mining task with numerous practic...
Detecting anomalies in dynamic graphs is a vital task, with numerous
pra...
Recently, recommender systems have been able to emit substantially impro...
Information systems have widely been the target of malware attacks.
Trad...
Graph representation learning, aiming to learn low-dimensional
represent...
Graph neural network (GNN), as a powerful representation learning model ...
Program or process is an integral part of almost every IT/OT system. Can...
Nowadays, multivariate time series data are increasingly collected in va...
The Nonlinear autoregressive exogenous (NARX) model, which predicts the
...