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Found 13 papers, 3 papers with code
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
Relational learning is an essential task in the domain of knowledge representation, particularly in knowledge graph completion (KGC). While relational learning in traditional single-modal settings has been extensively studied, exploring it within a multimodal KGC context presents distinct challenges and opportunities.
Multi-modal preference alignment remedies regression of visual instruction tuning on language model
In conclusion, we propose a distillation-based multi-modal alignment model with fine-grained annotations on a small dataset that reconciles the textual and visual performance of MLLMs, restoring and boosting language capability after visual instruction tuning.
Large Language Model based Multi-Agents: A Survey of Progress and Challenges
To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges.
Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout
With the widespread adoption of generative models, the Variational Autoencoder(VAE) framework has typically been employed to tackle challenges in reaction prediction, where the reactants are encoded as a condition for the decoder, which then generates the product.
LogicRec: Recommendation with Users' Logical Requirements
In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec.
Knowledge Distillation on Graphs: A Survey
Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowledge from data, leading to model compression and performance improvement.
TAR: Neural Logical Reasoning across TBox and ABox
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
A Generic Knowledge Based Medical Diagnosis Expert System
The proposed disease diagnosis system also uses a graphical user interface (GUI) to facilitate users to interact with the expert system.
A Simple and Debiased Sampling Method for Personalized Ranking
Pairwise ranking models have been widely used to address various problems, such as recommendation.
Scientific Dataset Discovery via Topic-level Recommendation
Data intensive research requires the support of appropriate datasets.
SAIL: Self-Augmented Graph Contrastive Learning
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario.
Addressing Class-Imbalance Problem in Personalized Ranking
Pairwise ranking models have been widely used to address recommendation problems.
