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Found 7 papers, 5 papers with code
On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning
The task in Compositional Zero-Shot learning (CZSL) is to learn composition of primitive concepts, i. e. objects and states, in such a way that even their novel compositions can be zero-shot classified.
Variational Embeddings for Community Detection and Node Representation
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
VECoDeR - Variational Embeddings for Community Detection and Node Representation
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
Metapath- and Entity-aware Graph Neural Network for Recommendation
In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures.
Ranked #1 on
Link Prediction
on MovieLens 25M
Compositional Learning of Image-Text Query for Image Retrieval
In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.
Ranked #1 on
Image Retrieval with Multi-Modal Query
on FashionIQ
Epitomic Variational Graph Autoencoder
Variational autoencoder (VAE) is a widely used generative model for learning latent representations.
Mend The Learning Approach, Not the Data: Insights for Ranking E-Commerce Products
In the literature, it is proposed to employ user feedback (such as clicks, add-to-basket (AtB) clicks and orders) to generate relevance judgments.
