Search Results for author:
Found 8 papers, 1 papers with code
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification
To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision.
Can Small Language Models be Good Reasoners for Sequential Recommendation?
We encode the generated rationales from the student model into a dense vector, which empowers recommendation in both ID-based and ID-agnostic scenarios.
Intent-aware Recommendation via Disentangled Graph Contrastive Learning
The other is different behaviors have different intent distributions, so how to establish their relations for a more explainable recommender system.
GraphEdit: Large Language Models for Graph Structure Learning
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures.
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks.
Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction
The core of our method is to put the object nearby diffuse walls and augment the LOS scan in the front view with the NLOS scans from the surrounding walls, which serve as virtual ``mirrors'' to trap lights toward the object.
Enhancing Non-line-of-sight Imaging via Learnable Inverse Kernel and Attention Mechanisms
Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem.
Ensemble Multi-Relational Graph Neural Networks
This EMR optimization objective is able to derive an iterative updating rule, which can be formalized as an ensemble message passing (EnMP) layer with multi-relations.
