Search Results for author:
Found 11 papers, 2 papers with code
Resource Mention Extraction for MOOC Discussion Forums
We propose the novel problem of learning resource mention identification in MOOC forums.
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages.
The MUIR Framework: Cross-Linking MOOC Resources to Enhance Discussion Forums
We demonstrate the feasibility of this approach to the automatic identification, linking and resolution -- a task known as Wikification -- of learning resources mentioned on MOOC discussion forums, from a harvested collection of 100K+ resources.
Cross-Modal Alignment with Mixture Experts Neural Network for Intral-City Retail Recommendation
At top of the MoE layer, we deploy a transformer layer for each task as task tower to learn task-specific information.
Reinforced Multi-Teacher Selection for Knowledge Distillation
When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation.
A Graph Neural Network Approach for Product Relationship Prediction
By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges.
Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks
Many real-world systems can be expressed in temporal networks with nodes playing far different roles in structure and function and edges representing the relationships between nodes.
Pruning with Compensation: Efficient Channel Pruning for Deep Convolutional Neural Networks
In this paper, a highly efficient pruning method is proposed to significantly reduce the cost of pruning DCNN.
Identifying critical nodes in complex networks by graph representation learning
Because of its wide application, critical nodes identification has become an important research topic at the micro level of network science.
Variational Model Inversion Attacks
In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.
A-Scan2BIM: Assistive Scan to Building Information Modeling
This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Modeling (BIM) applications.
