arXiv:2606.16388v1 Announce Type: new Abstract: High-dimensional incomplete (HDI) tensors are widely used in traffic and climate applications, but sparse observations make accurate completion difficult. The intrinsic non-linear dynamics and non-stationary variations across distinct multi-modal fields severely hinder the efficacy of conventional linear reconstruction frameworks. Neural Tucker factorization provides an effective framework for modeling high-order interactions among tensor modes. By parameterizing underlying structural characteristics into continuous latent spaces, neural represen
The paper introduces new methods for neural Tucker factorization, prompted by the increasing use of high-dimensional incomplete tensors in real-world applications and the limitations of conventional linear models for non-linear dynamics.
This development improves the accuracy and robustness of tensor completion and analysis, which is critical for applications in climate, traffic, and other fields relying on complex, incomplete datasets.
The ability to more effectively handle sparse, high-dimensional data with non-linear dynamics through neural networks enhances data interpretation and predictive capabilities across various scientific and applied domains.
- · AI researchers
- · Climate scientists
- · Traffic management systems
- · Data analytics platforms
- · Traditional linear reconstruction frameworks
- · Systems relying on incomplete or inaccurate tensor data
More accurate models become possible for complex, incomplete datasets like climate patterns or urban traffic flows.
Improved predictive capabilities could lead to more efficient resource allocation and better decision-making in affected sectors.
Enhanced data modeling could accelerate scientific discovery and the development of new AI applications in diverse fields.
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Read at arXiv cs.LG