$\nabla$SD: Differentiable Programming for Sparse Tensors
This addresses a fundamental bottleneck for applications in machine learning, NLP, and scientific computing by enabling efficient sparse tensor differentiation.
The paper tackles the problem of inefficient gradient computation for sparse tensors in differentiable programming, introducing a novel framework that outperforms state-of-the-art methods in performance and scalability on synthetic and real-world datasets.
Sparse tensors are prevalent in many data-intensive applications, yet existing differentiable programming frameworks are tailored towards dense tensors. This presents a significant challenge for efficiently computing gradients through sparse tensor operations, as their irregular sparsity patterns can result in substantial memory and computational overheads. In this work, we introduce a novel framework that enables the efficient and automatic differentiation of sparse tensors, addressing this fundamental issue. Our experiments demonstrate the effectiveness of the proposed framework in terms of performance and scalability, outperforming state-of-the-art frameworks across a range of synthetic and real-world datasets. Our approach offers a promising direction for enabling efficient and scalable differentiable programming with sparse tensors, which has significant implications for numerous applications in machine learning, natural language processing, and scientific computing.