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Found 37 papers, 13 papers with code
VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism
Large Vision-Language Models (LVLMs) have shown exceptional performance in multimodal tasks, but their effectiveness in complex visual reasoning is still constrained, especially when employing Chain-of-Thought prompting techniques.
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Currently, existing surveys on LLM safety primarily focus on specific stages of the LLM lifecycle, e. g., deployment phase or fine-tuning phase, lacking a comprehensive understanding of the entire "lifechain" of LLMs.
MuseFace: Text-driven Face Editing via Diffusion-based Mask Generation Approach
The Text-to-Mask diffusion model provides \textit{diversity} and \textit{flexibility} to the framework, while the semantic-aware face editing model ensures \textit{controllability} of the framework.
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling
Cross-domain recommendation (CDR) is a task that aims to improve the recommendation performance in a target domain by leveraging the information from source domains.
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations.
EvoFlow: Evolving Diverse Agentic Workflows On The Fly
The past two years have witnessed the evolution of large language model (LLM)-based multi-agent systems from labor-intensive manual design to partial automation (\textit{e. g.}, prompt engineering, communication topology) and eventually to fully automated design.
Hgformer: Hyperbolic Graph Transformer for Recommendation
The cold start problem is a challenging problem faced by most modern recommender systems.
PointTalk: Audio-Driven Dynamic Lip Point Cloud for 3D Gaussian-based Talking Head Synthesis
Furthermore, we integrate the audio-point enhancement module, which not only ensures the synchronization of the audio signal with the corresponding lip point cloud within the feature space, but also facilitates a deeper understanding of the interrelations among cross-modal conditional features.
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
We then present an analysis of decomposing the optimization target into a consistency penalty and a structure modification based on cause-effect relations.
Ranked #50 on
Node Classification
on Squirrel
On the Limitations and Prospects of Machine Unlearning for Generative AI
Generative AI (GenAI), which aims to synthesize realistic and diverse data samples from latent variables or other data modalities, has achieved remarkable results in various domains, such as natural language, images, audio, and graphs.
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System
Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been gaining more attention in recent years.
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
To search for optimal lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural Architecture Search with Graph SparsIfication and Network Pruning (GASSIP) method.
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years.
Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning
Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment.
One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
Large Language Models (LLMs) have advanced rapidly but face significant memory demands.
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Text-to-image diffusion models have achieved remarkable success in generating photorealistic images.
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including question answering and controlled text generation.
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11. 8% and 20. 7%, respectively.
Path-based Explanation for Knowledge Graph Completion
Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years.
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Recently years have witnessed a rapid development of large language models (LLMs).
Adversarially Robust Neural Architecture Search for Graph Neural Networks
To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA).
TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities.
Ranked #3 on
Motion Synthesis
on AIST++
Knowledge Graph Completion with Counterfactual Augmentation
With a carefully designed instantiation of a causal model on the knowledge graph, we generate the counterfactual relations to answer the question by regarding the representations of entity pair given relation as context, structural information of relation-aware neighborhood as treatment, and validity of the composed triplet as the outcome.
Improving Generalization of Meta-Learning With Inverted Regularization at Inner-Level
Despite the broad interest in meta-learning, the generalization problem remains one of the significant challenges in this field.
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications.
Semi-Supervised Hierarchical Graph Classification
We study the node classification problem in the hierarchical graph where a 'node' is a graph instance.
Not All Low-Pass Filters are Robust in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.
Meta Learning with Minimax Regularization
Even though meta-learning has attracted research wide attention in recent years, the generalization problem of meta-learning is still not well addressed.
Online Continual Adaptation with Active Self-Training
Our theoretical results show that OSAMD can fast adapt to changing environments with active queries.
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
Implicit Graph Neural Networks
Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data.
Spectral Graph Attention Network with Fast Eigen-approximation
In this paper, we first introduce the attention mechanism in the spectral domain of graphs and present Spectral Graph Attention Network (SpGAT) that learns representations for different frequency components regarding weighted filters and graph wavelets bases.
Power up! Robust Graph Convolutional Network based on Graph Powering
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
Octave Graph Convolutional Network
Many variants of Graph Convolutional Networks (GCNs) for representation learning have been proposed recently and have achieved fruitful results in various domains.
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.
Power up! Robust Graph Convolutional Network via Graph Powering
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
