Search Results for author:
Found 15 papers, 5 papers with code
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization
To fix this gap, we propose a model-independent Fairness-aware framework for SpatioTemporal Graph learning (FairSTG), which inherits the idea of exploiting advantages of well-learned samples to challenging ones with collaborative mix-up.
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
Motivated by complementary learning in neuroscience, we introduce a prompt-based complementary spatiotemporal learning termed ComS2T, to empower the evolution of models for data adaptation.
Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning
This approach allows the model to perceive the uncertainty of pseudo-labels at different training stages, thereby adaptively adjusting the selection thresholds for different classes.
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges.
A optimization framework for herbal prescription planning based on deep reinforcement learning
We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark.
Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction
In this paper, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end Knowledge graph completion model for Disease Gene Prediction using interactional tensor decomposition (called KDGene).
A Pre-training Framework for Knowledge Graph Completion
Knowledge graph completion (KGC) is one of the effective methods to identify new facts in knowledge graph.
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection.
Re-examining Distillation For Continual Object Detection
Training models continually to detect and classify objects, from new classes and new domains, remains an open problem.
Memory Replay with Data Compression for Continual Learning
In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.
TCMPR: TCM Prescription recommendation based on subnetwork term mapping and deep learning
In addition, the comprehensive experiments of TCMPR with different hyper parameters (i. e., feature embedding, feature dimension and feature fusion) that demonstrates that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.
Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images
We use the similarity graph as a regularizer in the loss function of a CNN model to minimize the distance between the input images and their k-nearest neighbours in the similarity graph while minimizing the categorical cross-entropy loss between the training image predictions and the actual image class labels.
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications.
A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease
The reliable and effective evaluation of early dementia has become essential research with medical imaging technologies and computer-aided algorithms.
