20 citations · 83 across the 17 of their papers we have counts for
17 papers
Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
Xu Yao, Siyuan Zhou, Zhenbo Wu +6
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, la…
Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting
Shuang Liang, Chaochuan Hou, Xu Yao +4
While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component-leve…
StockMem: An Event-Reflection Memory Framework for Stock Forecasting
He Wang, Wenyilin Xiao, Songqiao Han +1
Stock price prediction is challenging due to market volatility and its sensitivity to real-time events. While large language models (LLMs) offer new avenues for text-based forecast…
TSGym: Design Choices for Deep Multivariate Time-Series Forecasting
Shuang Liang, Chaochuan Hou, Xu Yao +4
Recently, deep learning has driven significant advancements in multivariate time series forecasting (MTSF) tasks. However, much of the current research in MTSF tends to evaluate mo…
Sample Design Engineering: An Empirical Study of What Makes Good Downstream Fine-Tuning Samples for LLMs
Biyang Guo, He Wang, Wenyilin Xiao +4
In the burgeoning field of Large Language Models (LLMs) like ChatGPT and LLaMA, Prompt Engineering (PE) is renowned for boosting zero-shot or in-context learning (ICL) through prom…
Enhancing Molecular Property Prediction via Mixture of Collaborative Experts
Xu Yao, Shuang Liang, Songqiao Han +1
Molecular Property Prediction (MPP) task involves predicting biochemical properties based on molecular features, such as molecular graph structures, contributing to the discovery o…
Anomaly Detection with Score Distribution Discrimination
Minqi Jiang, Songqiao Han, Hailiang Huang
Recent studies give more attention to the anomaly detection (AD) methods that can leverage a handful of labeled anomalies along with abundant unlabeled data. These existing anomaly…
Weakly Supervised Anomaly Detection: A Survey
Minqi Jiang, Chaochuan Hou, Ao Zheng +6
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.…
Practitioners' Expectations on Code Completion
Chaozheng Wang, Junhao Hu, Cuiyun Gao +5
Code completion has become a common practice for programmers during their daily programming activities. It aims at automatically predicting the next tokens or lines that the progra…
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
Biyang Guo, Yeyun Gong, Yelong Shen +4
We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual s…
IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification
Ziyuan Wang, Hailiang Huang, Songqiao Han
Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in…
Selective Text Augmentation with Word Roles for Low-Resource Text Classification
Biyang Guo, Songqiao Han, Hailiang Huang
Data augmentation techniques are widely used in text classification tasks to improve the performance of classifiers, especially in low-resource scenarios. Most previous methods con…
ADBench: Anomaly Detection Benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang +2
Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of a…
Neural News Recommendation with Event Extraction
Songqiao Han, Hailiang Huang, Jiangwei Liu
A key challenge of online news recommendation is to help users find articles they are interested in. Traditional news recommendation methods usually use single news information, wh…
American Hate Crime Trends Prediction with Event Extraction
Songqiao Han, Hailiang Huang, Jiangwei Liu +1
Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes. The FBI's Uniform Cr…
What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification
Biyang Guo, Sonqiao Han, Hailiang Huang
Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios. Whilst lots of creativ…
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Xiao Han, Ruiqing Ding, Leye Wang +1
Credit investigation is critical for financial services. Whereas, traditional methods are often restricted as the employed data hardly provide sufficient, timely and reliable infor…