Search Results for author:
Found 35 papers, 8 papers with code
Weakly Supervised Spatial Implicit Neural Representation Learning for 3D MRI-Ultrasound Deformable Image Registration in HDR Prostate Brachytherapy
For the institutional dataset, prostate CTV achieved DSC $0. 88 \pm 0. 09$, MSD $1. 21 \pm 0. 38$ mm, and HD95 $2. 09 \pm 1. 48$ mm.
ZiGong 1.0: A Large Language Model for Financial Credit
Large Language Models (LLMs) have demonstrated strong performance across various general Natural Language Processing (NLP) tasks.
Correcting Large Language Model Behavior via Influence Function
Recent advancements in AI alignment techniques have significantly improved the alignment of large language models (LLMs) with static human preferences.
Precision-Enhanced Human-Object Contact Detection via Depth-Aware Perspective Interaction and Object Texture Restoration
Human-object contact (HOT) is designed to accurately identify the areas where humans and objects come into contact.
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis
To address this issue, we propose a novel method that integrates functional and structural connectivity based on heterogeneous graph neural networks (HGNNs) to better leverage the rich heterogeneity in dual-modal images.
FairMindSim: Alignment of Behavior, Emotion, and Belief in Humans and LLM Agents Amid Ethical Dilemmas
To explore the various socioeconomic motivations, which we refer to as beliefs, that drive both humans and LLM agents as bystanders to intervene in unjust situations involving others, and how these beliefs interact to influence individual behavior, we incorporated knowledge from relevant sociological fields and proposed the Belief-Reward Alignment Behavior Evolution Model (BREM) based on the recursive reward model (RRM).
A Review of Human-Object Interaction Detection
Human-object interaction (HOI) detection plays a key role in high-level visual understanding, facilitating a deep comprehension of human activities.
PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation
In weakly supervised medical image segmentation, the absence of structural priors and the discreteness of class feature distribution present a challenge, i. e., how to accurately propagate supervision signals from local to global regions without excessively spreading them to other irrelevant regions?
A Generative Approach to Credit Prediction with Learnable Prompts for Multi-scale Temporal Representation Learning
Recent industrial credit scoring models remain heavily reliant on manually tuned statistical learning methods.
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.
CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state
However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating.
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 methods depend on extensively annotated image datasets, which require a significant amount of manpower.
CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning
Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states.
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.
TED-Net: Dispersal Attention for Perceiving Interaction Region in Indirectly-Contact HOI Detection
Human-Object Interaction (HOI) detection is a fertile research ground that merits further investigation in computer vision, and plays an important role in image high-level semantic information understanding.
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.
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification
Low-shot image classification is a fundamental task in computer vision and the emergence of large-scale vision-language models such as CLIP has greatly advanced the forefront of research in this field.
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
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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.
