Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Structured Transforms for Small-Footprint Deep Learning
Authors: Vikas Sindhwani, Tara Sainath, Sanjiv Kumar
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that these transforms can significantly accelerate inference and forward/backward passes during training, and offer superior accuracy-compactness-speed tradeoffs in comparison to a number of existing techniques. |
| Researcher Affiliation | Industry | Vikas Sindhwani Tara N. Sainath Sanjiv Kumar Google, New York EMAIL |
| Pseudocode | Yes | Theorem 3.3 (Fast Multiplication). Given an n b matrix X, the matrix-matrix product, Y = (Pr i=1 Z1(gi)Z 1(hi)) X, can be computed at the cost of 2(rb + b + r) FFTs, using the following algorithm. Set η = [1, η, η2 . . . ηn 1]T where η = ( 1) 1 n = exp(i π n) Initialize Y = 0n b Set X = fft(diag(η)X) Set G = fft(G) = [ g1 . . . gr] and H = fft(diag(η)H) = [ h1 . . . hr] for i = 1 to r U = Z 1(hi)X = diag( η)ifft diag( hi) X V = diag( gi) fft(U) Y = Y + V Set Y = ifft (Y) Return Y |
| Open Source Code | No | The paper cites supplementary material at http://vikas.sindhwani.org/st_supplementary.pdf, which is a PDF, and references a third-party library FFTW, but does not provide an explicit link or statement about releasing its own source code. |
| Open Datasets | Yes | MNIST is the original 10-class MNIST digit classification dataset with 60000 training examples and 10000 test examples. We refer the reader to [23] for more details about the datasets. (Reference [23] is: T. Sainath and C. Parada. Convolutional neural networks for small-footprint keyword spotting. In Proc. Interspeech, 2015.) |
| Dataset Splits | Yes | The utterances were randomly split into training, development and evaluation sets in the ratio of 80 : 5 : 15. |
| Hardware Specification | Yes | 6-core 32-GB Intel(R) Xeon(R) machine; random datasets. |
| Software Dependencies | No | The paper mentions "FFT implementations (we use FFTW: http://www.fftw.org)" but does not specify a version number for FFTW or any other software components. |
| Experiment Setup | Yes | The global learning rate is set to 0.002, while our structured transform layers use a layer-specific learning rate of 0.0005; both are decayed by an exponential factor of 0.1. |