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Found 9 papers, 5 papers with code
Personalize Anything for Free with Diffusion Transformer
Personalized image generation aims to produce images of user-specified concepts while enabling flexible editing.
Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs
To address these issues, we leverage Large Language Models (LLMs) for semantic augmentation thanks to the LLMs' extra knowledge and ability of integrating knowledge from both LLMs and KGs.
Parse Trees Guided LLM Prompt Compression
It first obtains a parse tree for each sentence based on linguistic rules, and calculates local information entropy for each node in a parse tree.
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models
To leverage the advantages of both approaches in molecular property prediction, we propose a novel Molecular Graph representation learning framework that integrates Large language models and Domain-specific small models (MolGraph-LarDo).
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores.
Federated Learning with Classifier Shift for Class Imbalance
Federated learning aims to learn a global model collaboratively while the training data belongs to different clients and is not allowed to be exchanged.
Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work.
Learning to Decompose Visual Features with Latent Textual Prompts
Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations.
Boost Neural Networks by Checkpoints
In the experiments, it yields up to 5. 02% higher accuracy over single EfficientNet-B0 on the imbalanced datasets.
