Search Results for author:
Found 15 papers, 4 papers with code
RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space
Knowledge graph embedding, which aims to learn representations of entities and relations in knowledge graphs, finds applications in various downstream tasks.
Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts
Aspect-based summarization has seen significant advancements, especially in structured text.
Neural Node Matching for Multi-Target Cross Domain Recommendation
To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i. e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions.
Capturing Topic Framing via Masked Language Modeling
Differential framing of issues can lead to divergent world views on important issues.
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations
The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks.
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes.
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view.
A Survey of Deep Causal Models and Their Industrial Applications
The notion of causality assumes a paramount position within the realm of human cognition.
Cross-Architecture Self-supervised Video Representation Learning
In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning.
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
In this paper, we propose a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously.
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content.
Emotion-based Modeling of Mental Disorders on Social Media
Our proposed model is different from other work in this area in that our model is based entirely on the emotional states, and the transition between these states of users on Reddit, whereas prior work is typically based on content-based representations (e. g., n-grams, language model embeddings, etc).
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors.
Thermodynamics with pressure and volume of black holes based on two assumptions under scalar field scattering
Recently, a new assumption was proposed in [Phys.
General Relativity and Quantum Cosmology
Multi-modal Identification of State-Sponsored Propaganda on Social Media
The prevalence of state-sponsored propaganda on the Internet has become a cause for concern in the recent years.
