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Found 18 papers, 3 papers with code
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding
The dataset is created with GPT-4 to extend the abstract concept to a scene and concrete objects.
Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters.
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction
Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry.
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems.
A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder
The fairness issue of clinical data modeling, especially on Electronic Health Records (EHRs), is of utmost importance due to EHR's complex latent structure and potential selection bias.
Mini-PointNetPlus: a local feature descriptor in deep learning model for 3d environment perception
The pioneer work PointNet has been widely applied as a local feature descriptor, a fundamental component in deep learning models for 3D perception, to extract features of a point cloud.
LEST: Large-scale LiDAR Semantic Segmentation with Transformer
Large-scale LiDAR-based point cloud semantic segmentation is a critical task in autonomous driving perception.
Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs
Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between products or product types, which can be utilized in recommender systems.
Towards Spatio-temporal Sea Surface Temperature Forecasting via Static and Dynamic Learnable Personalized Graph Convolution Network
Sea surface temperature (SST) is uniquely important to the Earth's atmosphere since its dynamics are a major force in shaping local and global climate and profoundly affect our ecosystems.
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs.
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram.
Mitigating Health Disparities in EHR via Deconfounder
First, these methods usually mean a trade-off between the model's performance and fairness.
MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds
The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.
Dynamic Graph Collaborative Filtering
Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.
Basket Recommendation with Multi-Intent Translation Graph Neural Network
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.
Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation
Driving styles have a great influence on vehicle fuel economy, active safety, and drivability.
