Search Results for author:
Found 9 papers, 5 papers with code
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance.
Ranked #17 on
Node Classification
on Texas
On Synthetic Data for Back Translation
Back translation (BT) is one of the most significant technologies in NMT research fields.
DistillCSE: Distilled Contrastive Learning for Sentence Embeddings
This paper proposes the DistillCSE framework, which performs contrastive learning under the self-training paradigm with knowledge distillation.
Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors
Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage.
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives
This paper improves contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption.
Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration
Re-GAN stabilizes the GANs models with less data and offers an alternative to the existing GANs tickets and progressive growing methods.
Modulation and Classification of Mixed Signals Based on Deep Learning
Second, we investigate some deep learning models based on CNN (ResNet34, hierarchical structure) and other deep learning models (LSTM, CLDNN).
DisenE: Disentangling Knowledge Graph Embeddings
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently.
LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network
In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels.
Multi Label Text Classification
Multi-Label Text Classification
+1
