Search Results for author:
Found 7 papers, 4 papers with code
Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery
To enable better knowledge transfer, we design a prototype learning method integrating the supervised and pseudo signals from IND and OOD samples.
A Survey of Ontology Expansion for Conversational Understanding
This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding.
Retrieval Augmented Generation for Dynamic Graph Modeling
This approach presents two critical challenges: (1) How to identify and retrieve high-quality demonstrations that are contextually and temporally analogous to dynamic graph samples?
Exploring the Potential of Large Language Models for Heterophilic Graphs
In the second stage, we adaptively manage message propagation in GNNs for different edge types based on node features, structures, and heterophilic or homophilic characteristics.
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
The techniques can be broadly categorized into meta-learning, pre-training, and hybrid approaches, with a finer-grained classification in each category to aid readers in their method selection process.
Actively Discovering New Slots for Task-oriented Conversation
Existing task-oriented conversational search systems heavily rely on domain ontologies with pre-defined slots and candidate value sets.
Multi-Sample based Contrastive Loss for Top-k Recommendation
Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation.
