压缩深度神经网络：使用Kac随机游走矩阵的新哈希管道
Compressing Deep Neural Networks: A New Hashing Pipeline Using Kac's Random Walk Matrices
摘要：
深度学习的普及程度与日俱增。然而，尽管最近在硬件方面取得了进步，但深度神经网络仍然是计算密集型的。最近的工作表明，通过保持向量之间的角距离，随机特征映射能够降低维度，而不会对估计量引入偏差。我们测试了各种已建立的散列管道以及使用Kac的随机游走矩阵的新方法。我们证明，这种方法达到了类似的精度，现有的管道。
英文摘要：
The popularity of deep learning is increasing by the day. However, despite the recent advancements in hardware, deep neural networks remain computationally intensive. Recent work has shown that by preserving the angular distance between vectors, random feature maps are able to reduce dimensionality without introducing bias to the estimator. We test a variety of established hashing pipelines as well as a new approach using Kac's random walk matrices. We demonstrate that this method achieves similar accuracy to existing pipelines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
