A thesis that synthesizes the author's published work on continuous approximations to discrete deep learning problems, with experiments showing efficiency gains, but offering little new beyond the author's prior papers.
SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions.arXiv:2311.14114,
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Principled Approximation Methods for Efficient and Scalable Deep Learning
A thesis that synthesizes the author's published work on continuous approximations to discrete deep learning problems, with experiments showing efficiency gains, but offering little new beyond the author's prior papers.