Automated self-supervised learning for recommendation

L Xia, C Huang, C Huang, K Lin, T Yu… - Proceedings of the ACM …, 2023 - dl.acm.org
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for
collaborative filtering (CF). To improve the representation quality over limited labeled data …

On the equivalence between positional node embeddings and structural graph representations

B Srinivasan, B Ribeiro - arXiv preprint arXiv:1910.00452, 2019 - arxiv.org
This work provides the first unifying theoretical framework for node (positional) embeddings
and structural graph representations, bridging methods like matrix factorization and graph …

The functional neural process

C Louizos, X Shi, K Schutte… - Advances in Neural …, 2019 - proceedings.neurips.cc
We present a new family of exchangeable stochastic processes, the Functional Neural
Processes (FNPs). FNPs model distributions over functions by learning a graph of …

Fast variational autoencoder with inverted multi-index for collaborative filtering

J Chen, D Lian, B Jin, X Huang, K Zheng… - Proceedings of the ACM …, 2022 - dl.acm.org
Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for
collaborative filtering. However, the bottleneck of VAE lies in the softmax computation over …

Deep conditional gaussian mixture model for constrained clustering

L Manduchi, K Chin-Cheong… - Advances in …, 2021 - proceedings.neurips.cc
Constrained clustering has gained significant attention in the field of machine learning as it
can leverage prior information on a growing amount of only partially labeled data. Following …

DGNet: distribution guided efficient learning for oil spill image segmentation

F Chen, H Balzter, F Zhou, P Ren… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Successful implementation of oil spill segmentation in synthetic aperture radar (SAR)
images is vital for marine environmental protection. In this article, we develop an effective …

Improving graph neural networks with structural adaptive receptive fields

X Ma, J Wang, H Chen, G Song - Proceedings of the Web Conference …, 2021 - dl.acm.org
The abundant information in graphs helps us to learn more expressive node
representations. Different nodes in the neighborhood have different importance to the …

Robot-dependent traversability estimation for outdoor environments using deep multimodal variational autoencoders

M Eder, G Steinbauer-Wagner - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Efficient and reliable navigation in off-road environments poses a significant challenge for
robotics, especially when factoring in the varying capabilities of robots across different …

Field-aware variational autoencoders for billion-scale user representation learning

G Fan, C Zhang, J Chen, B Li, Z Xu, Y Li… - 2022 IEEE 38th …, 2022 - ieeexplore.ieee.org
User representation learning plays an essential role in Internet applications, such as
recommender systems. Though developing a universal embedding for users is demanding …

Bayesian graph convolutional network with partial observations

S Luo, P Liu, X Ye - Plos one, 2024 - journals.plos.org
As a widely studied model in the machine learning and data processing society, graph
convolutional network reveals its advantage in non-grid data processing. However, existing …