Search Results for author:
Found 7 papers, 2 papers with code
Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning
With the rapid evolution of the Internet and the exponential proliferation of information, users encounter information overload and the conundrum of choice.
MatchSeg: Towards Better Segmentation via Reference Image Matching
Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set.
Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques
In the realm of computational knowledge representation, Knowledge Graph Reasoning (KG-R) stands at the forefront of facilitating sophisticated inferential capabilities across multifarious domains.
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration
Proteins are essential for life, and their structure determines their function.
Protein Language Model
Protein Secondary Structure Prediction
+1
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
To this end, we suggest that generated data (with mistaken OOD generation) can be used to devise an auxiliary OOD detection task to facilitate real OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i. e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i. e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control.
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification
Compared with the existing literature, our WNFG of EEG signals achieves up to 10 times of redundant edge reduction, and our approach achieves up to 97 times of model pruning without loss of classification accuracy.
