arXiv:2606.15679v1 Announce Type: cross Abstract: Stochastic trace estimation is a standard tool for approximating the trace of a large-scale matrix available only through matrix-vector products. However, in tensor-structured settings, unstructured Gaussian or Rademacher test vectors may be prohibitively expensive to store and compute with, while cheaper rank-one tensor-product vectors can require sample complexities that grow exponentially with the tensor order. This work studies Gaussian random tensor train vectors as a structured alternative for stochastic trace estimation. We show that, wi
The continuous growth in scale and complexity of AI/ML models necessitates more efficient and scalable computational linear algebra methods.
This research addresses a fundamental bottleneck in high-dimensional tensor computations, which is critical for the development and training of advanced AI systems.
The proposed method offers a more resource-efficient approach to a core computational problem in AI, potentially enabling larger models or faster training with existing hardware.
- · AI/ML researchers
- · Cloud computing providers
- · AI model developers
- · High-performance computing sector
- · Inefficient tensor computation methods
More efficient training and deployment of large-scale AI models become possible.
This could lead to a reduction in the computational cost associated with certain AI development tasks.
Improved efficiency in AI could accelerate the development of more complex autonomous systems across various industries.
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Read at arXiv cs.LG