Search Results for author:
Found 14 papers, 3 papers with code
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting.
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing
Our findings indicate the learning effectiveness of incorporating hashing techniques within the exploration of bipartite graph reception fields, as opposed to simply treating hashing as post-processing to output embeddings.
A Survey on Deep Active Learning: Recent Advances and New Frontiers
Active learning seeks to achieve strong performance with fewer training samples.
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Node importance estimation problem has been studied conventionally with homogeneous network topology analysis.
HiHPQ: Hierarchical Hyperbolic Product Quantization for Unsupervised Image Retrieval
Existing unsupervised deep product quantization methods primarily aim for the increased similarity between different views of the identical image, whereas the delicate multi-level semantic similarities preserved between images are overlooked.
Hyperbolic Representation Learning: Revisiting and Advancing
To address this, we propose a simple yet effective method, hyperbolic informed embedding (HIE), by incorporating cost-free hierarchical information deduced from the hyperbolic distance of the node to origin (i. e., induced hyperbolic norm) to advance existing \hlms.
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models.
Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space
We propose an end-to-end Bipartite Graph Convolutional Hashing approach, namely BGCH, which consists of three novel and effective modules: (1) adaptive graph convolutional hashing, (2) latent feature dispersion, and (3) Fourier serialized gradient estimation.
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
Learning vectorized embeddings is at the core of various recommender systems for user-item matching.
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation
However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability.
Click-Through Rate Prediction
Knowledge-Aware Recommendation
+1
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention.
A Literature Review of Recent Graph Embedding Techniques for Biomedical Data
With the rapid development of biomedical software and hardware, a large amount of relational data interlinking genes, proteins, chemical components, drugs, diseases, and symptoms has been collected for modern biomedical research.
