Dual-Attention Convolution Experts for Sparse Tensor Completion
Tensor factorization (TF) has been widely adopted for high-dimensional sparse data completion tasks. Despite significant progress, neural TF methods often struggle to capture complex cross-mode interactions and remain vulnerable to (extreme) data sparsity. To address these challenges, we propose a novel neural tensor factorization approach, termed Dual-Attention Convolution Expert Networks with Group-Level Contrastive Learning (DCGC). For the first problem, DCGC generates diverse non-linear alignment patterns of latent factors via a multi-channel convolution network, and leverages the gated dual-attention mechanism to drive the model to focus on more important output channels (i.e., convo...
| Authors | Yanlei Liu, Zhenyu Liao |
|---|---|
| Primary category | cs.LG |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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