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Found 13 papers, 5 papers with code
TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning
Charts are important for presenting and explaining complex data relationships.
VersaT2I: Improving Text-to-Image Models with Versatile Reward
Recent text-to-image (T2I) models have benefited from large-scale and high-quality data, demonstrating impressive performance.
URHand: Universal Relightable Hands
To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities.
Devil in the Number: Towards Robust Multi-modality Data Filter
In order to appropriately filter multi-modality data sets on a web-scale, it becomes crucial to employ suitable filtering methods to boost performance and reduce training costs.
A Survey on Pretrained Language Models for Neural Code Intelligence
As the complexity of modern software continues to escalate, software engineering has become an increasingly daunting and error-prone endeavor.
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms
However, with wide applications of deep learning in robotic arms, there are new challenges such as the allocation of grasping computing power and the growing demand for security.
A Survey on Deep Graph Generation: Methods and Applications
In this paper, we conduct a comprehensive review on the existing literature of deep graph generation from a variety of emerging methods to its wide application areas.
An Empirical Study of Graph Contrastive Learning
We envision this work to provide useful empirical evidence of effective GCL algorithms and offer several insights for future research.
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data.
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.
Graph Contrastive Learning with Adaptive Augmentation
On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information.
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments.
Deep Graph Contrastive Representation Learning
Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.
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