376 citations · 413 across the 18 of their papers we have counts for
18 papers
MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation
Wenqing Tian, Hanyi Mao, Zhaocheng Liu +4
Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-reference workflows, users may provi…
GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Wen Ye, Zhaocheng Liu, Yuwei Gui +6
Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and m…
Efficient Medical VIE via Reinforcement Learning
Lijun Liu, Ruiyang Li, Zhaocheng Liu +5
Visual Information Extraction (VIE) converts unstructured document images into structured formats like JSON, critical for medical applications such as report analysis and online co…
Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators
Zhaocheng Liu, Quan Tu, Wen Ye +7
Recently, large language models have shown great potential to transform online medical consultation. Despite this, most research targets improving diagnostic accuracy with ample in…
LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
Jian Jia, Yipei Wang, Yan Li +8
Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic…
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
Zhaocheng Liu, Zhongxiang Fan, Jian Liang +2
The one-epoch overfitting phenomenon has been widely observed in industrial Click-Through Rate (CTR) applications, where the model performance experiences a significant degradation…
Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation
Qiang Liu, Zhaocheng Liu, Zhenxi Zhu +2
Recently, multi-interest models, which extract interests of a user as multiple representation vectors, have shown promising performances for sequential recommendation. However, non…
Deep Stable Representation Learning on Electronic Health Records
Yingtao Luo, Zhaocheng Liu, Qiang Liu
Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I.D. hypo…
Improving Multi-Interest Network with Stable Learning
Zhaocheng Liu, Yingtao Luo, Di Zeng +4
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems. Due to the diverse nature of user interests, recent advances propose t…
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction
Yi Guo, Zhaocheng Liu, Jianchao Tan +6
In industry, feature selection is a standard but necessary step to search for an optimal set of informative feature fields for efficient and effective training of deep Click-Throug…
Deep Active Learning for Text Classification with Diverse Interpretations
Qiang Liu, Yanqiao Zhu, Zhaocheng Liu +2
Recently, Deep Neural Networks (DNNs) have made remarkable progress for text classification, which, however, still require a large number of labeled data. To train high-performing…
DNN2LR: Automatic Feature Crossing for Credit Scoring
Qiang Liu, Zhaocheng Liu, Haoli Zhang +2
Credit scoring is a major application of machine learning for financial institutions to decide whether to approve or reject a credit loan. For sake of reliability, it is necessary…
STAN: Spatio-Temporal Attention Network for Next Location Recommendation
Yingtao Luo, Qiang Liu, Zhaocheng Liu
The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical g…
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
Zhaocheng Liu, Qiang Liu, Haoli Zhang +1
For sake of reliability, it is necessary for models in real-world applications to be both powerful and globally interpretable. Simple classifiers, e.g., Logistic Regression (LR), a…
Deep Active Learning by Model Interpretability
Qiang Liu, Zhaocheng Liu, Xiaofang Zhu +1
Recent successes of Deep Neural Networks (DNNs) in a variety of research tasks, however, heavily rely on the large amounts of labeled samples. This may require considerable annotat…
Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective
Qiang Liu, Haoli Zhang, Zhaocheng Liu
In recent years, substantial progress has been made on Graph Convolutional Networks (GCNs). However, the computing of GCN usually requires a large memory space for keeping the enti…
An Empirical Study on Feature Discretization
Qiang Liu, Zhaocheng Liu, Haoli Zhang
When dealing with continuous numeric features, we usually adopt feature discretization. In this work, to find the best way to conduct feature discretization, we present some theore…
CPM-sensitive AUC for CTR prediction
Zhaocheng Liu, Guangxue Yin
The prediction of click-through rate (CTR) is crucial for industrial applications, such as online advertising. AUC is a commonly used evaluation indicator for CTR models. For adver…