Search Results for author:
Found 25 papers, 4 papers with code
FinLangNet: A Novel Deep Learning Framework for Credit Risk Prediction Using Linguistic Analogy in Financial Data
Our research demonstrates that FinLangNet surpasses traditional statistical methods in predicting credit risk and that its integration with these methods enhances credit card fraud prediction models, achieving a significant improvement of over 1. 5 points in the Kolmogorov-Smirnov metric.
Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations.
Relationship Discovery for Drug Recommendation
Medication recommendation systems are designed to deliver personalized drug suggestions that are closely aligned with individual patient needs.
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning
However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images.
HDR Imaging for Dynamic Scenes with Events
High dynamic range imaging (HDRI) for real-world dynamic scenes is challenging because moving objects may lead to hybrid degradation of low dynamic range and motion blur.
Bayesian Diffusion Models for 3D Shape Reconstruction
We present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes.
FreeA: Human-object Interaction Detection using Free Annotation Labels
Recent human-object interaction (HOI) detection approaches rely on high cost of manpower and require comprehensive annotated image datasets.
Dual-Granularity Medication Recommendation Based on Causal Inference
Medication recommendation aims to integrate patients' long-term health records with medical knowledge, recommending accuracy and safe medication combinations for specific conditions.
COPR: Continual Human Preference Learning via Optimal Policy Regularization
Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences.
CPPO: Continual Learning for Reinforcement Learning with Human Feedback
The approach of Reinforcement Learning from Human Feedback (RLHF) is widely used for enhancing pre-trained Language Models (LM), enabling them to better align with human preferences.
Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles
Reinforcement learning from human feedback (RLHF) emerges as a promising paradigm for aligning large language models (LLMs).
COPR: Continual Learning Human Preference through Optimal Policy Regularization
The technique of Reinforcement Learning from Human Feedback (RLHF) is a commonly employed method to improve pre-trained Language Models (LM), enhancing their ability to conform to human preferences.
FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly
While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation.
A Lower Bound of Hash Codes' Performance
By testing on a series of hash-models, we obtain performance improvements among all of them, with an up to $26. 5\%$ increase in mean Average Precision and an up to $20. 5\%$ increase in accuracy.
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Being equipped with three modules (i. e., global user behavior encoder, local multi-channel encoder, and region-aware weighting strategy), MCMG is capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the region check-in patterns.
Multi-Faceted Hierarchical Multi-Task Learning for a Large Number of Tasks with Multi-dimensional Relations
For example, when using MTL to model various user behaviors in RS, if we differentiate new users and new items from old ones, there will be a cartesian product style increase of tasks with multi-dimensional relations.
Capsule Graph Neural Networks with EM Routing
To effectively classify graph instances, graph neural networks need to have the capability to capture the part-whole relationship existing in a graph.
Self-adaptive Multi-task Particle Swarm Optimization
In the focus search strategy, if there is no knowledge source benefit the optimization of a task, then all knowledge sources in the task's pool are forbidden to be utilized except the task, which helps to improve the performance of the proposed algorithm.
Physical Artificial Intelligence: The Concept Expansion of Next-Generation Artificial Intelligence
In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Physicial Artifical Intelligence and Distributed Physicial Artifical Intelligence.
Geom-GCN: Geometric Graph Convolutional Networks
From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses.
Node Classification on Non-Homophilic (Heterophilic) Graphs
Representation Learning
+1
When Collaborative Filtering Meets Reinforcement Learning
In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations.
Generative Steganography by Sampling
In this paper, a new data-driven information hiding scheme called generative steganography by sampling (GSS) is proposed.
Semi-supervised Multimodal Hashing
As unsupervised multimodal hashing methods are usually inferior to supervised ones, while the supervised ones requires too much manually labeled data, the proposed method in this paper utilizes a part of labels to design a semi-supervised multimodal hashing method.
Content-based Influence Modeling for Opinion Behavior Prediction
The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors.
