Automated self-supervised learning for recommendation
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 …
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 …
and structural graph representations, bridging methods like matrix factorization and graph …
The functional neural process
We present a new family of exchangeable stochastic processes, the Functional Neural
Processes (FNPs). FNPs model distributions over functions by learning a graph of …
Processes (FNPs). FNPs model distributions over functions by learning a graph of …
Fast variational autoencoder with inverted multi-index for collaborative filtering
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 …
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 …
can leverage prior information on a growing amount of only partially labeled data. Following …
DGNet: distribution guided efficient learning for oil spill image segmentation
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 …
images is vital for marine environmental protection. In this article, we develop an effective …
Improving graph neural networks with structural adaptive receptive fields
The abundant information in graphs helps us to learn more expressive node
representations. Different nodes in the neighborhood have different importance to the …
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 …
robotics, especially when factoring in the varying capabilities of robots across different …
Field-aware variational autoencoders for billion-scale user representation learning
User representation learning plays an essential role in Internet applications, such as
recommender systems. Though developing a universal embedding for users is demanding …
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 …
convolutional network reveals its advantage in non-grid data processing. However, existing …