Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks
arXiv:2606.00130v1 Announce Type: new Abstract: We study Automatically Differentiable Nonlinear Tensor Networks (ADNTNs), a family of structured weight generators whose compact core tensors are trained end-to-end by reverse-mode automatic differentiation (AD). The approach can be viewed as a natural extension of low-rank adaptation and tensor factorisation: instead of using one low-rank matrix update, an ADNTN builds a large weight tensor through a hierarchy of small cores, nonlinear activations, and optional lateral mixing tensors. The paper focuses on three architectures: Tree Tensor Network
This research addresses a critical need for efficient deep learning models at a time when model size and computational demands are rapidly increasing, driven by the capabilities of reverse-mode automatic differentiation.
Exponential compression of deep neural networks can significantly reduce computational costs, memory footprint, and energy consumption, making advanced AI more accessible and sustainable.
The ability to build large weight tensors from compact core tensors enables the deployment of powerful DNNs in resource-constrained environments and could accelerate AI development and deployment cycles.
- · AI developers
- · Edge computing platforms
- · Mobile AI applications
- · Hardware manufacturers (benefiting from wider AI adoption)
- · Manufacturers of overly specialized, high-cost AI hardware
- · Traditional large-model deployment strategies
Reduced computational resource requirements for training and deploying large AI models.
Democratization of sophisticated AI capabilities, enabling broader innovation and application in resource-limited settings.
Acceleration of AI research and development cycles due to faster experimentation and lower infrastructure costs, potentially leading to more rapid advancements across various AI domains.
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Read at arXiv cs.LG