Why Reproducible Research
Reproducible research is a concept championed by many notable researchers that calls for public availability of the software environment that is used to generate results and draw conclusions in peer-reviewed scientific publications. The benefits of undertaking such an activity to our society and the scientific community are enormous, while the drawbacks are none. INSPIRE Lab is committed to this ideal of publicly sharing the requisite software/data sets associated with publications by its members and encourages other members of the research community to follow the lead of scores of prominent researchers and promote reproducibility of research through public sharing of their own software and data sets.
Paper – H. Raja and W.U. Bajwa, “Cloud K-SVD: A collaborative dictionary learning algorithm for big, distributed data,” IEEE Trans. Signal Processing, vol. 64, no. 1, pp. 173-188, Jan. 2016. Companion Code – Download from BitBucket. [BibTeX]
Paper – T. Wu and W.U. Bajwa, “Learning the nonlinear geometry of high-dimensional data: Models and algorithms,” IEEE Trans. Signal Processing, vol. 63, no. 23, pp. 6229-6244, Dec. 2015. Companion Code – Download from BitBucket. [BibTeX]
Paper – W.U. Bajwa, M.F. Duarte, and R. Calderbank, “Conditioning of random block subdictionaries with applications to block-sparse recovery and regression,” IEEE Trans. Information Theory, vol. 61, no. 7, pp. 4060-4079, Jul. 2015. Companion Code – Download from BitBucket. [BibTeX]
Paper – A. Harms, W.U. Bajwa, and R. Calderbank, “A constrained random demodulator for sub-Nyquist sampling,” IEEE Trans. Signal Processing, vol. 61, no. 3, pp. 707-723, Feb. 2013. Companion Code – Download from RunMyCode or GitHub. [BibTeX]
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