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1 code implementation • 29 Jun 2019 • Nikita Ivkin, Edo Liberty, Kevin Lang, Zohar Karnin, Vladimir Braverman

Approximating quantiles and distributions over streaming data has been studied for roughly two decades now.

no code implementations • 22 Jun 2019 • Nick Ryder, Zohar Karnin, Edo Liberty

In many applications the data set to be projected is given to us in advance, yet the current RP techniques do not make use of information about the data.

no code implementations • 11 Jun 2019 • Zohar Karnin, Edo Liberty

We provide general techniques for bounding the class discrepancy of machine learning problems.

1 code implementation • ICLR 2019 • Yu Bai, Yu-Xiang Wang, Edo Liberty

To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights.

2 code implementations • 17 Mar 2016 • Zohar Karnin, Kevin Lang, Edo Liberty

One of our contributions is a novel representation and modification of the widely used merge-and-reduce construction.

Data Structures and Algorithms

no code implementations • 8 Jan 2015 • Mina Ghashami, Edo Liberty, Jeff M. Phillips, David P. Woodruff

It performed $O(d \times \ell)$ operations per row and maintains a sketch matrix $B \in R^{\ell \times d}$ such that for any $k < \ell$ $\|A^TA - B^TB \|_2 \leq \|A - A_k\|_F^2 / (\ell-k)$ and $\|A - \pi_{B_k}(A)\|_F^2 \leq \big(1 + \frac{k}{\ell-k}\big) \|A-A_k\|_F^2 $ .

Data Structures and Algorithms 68W40 (Primary)

no code implementations • 18 Dec 2014 • Edo Liberty, Ram Sriharsha, Maxim Sviridenko

We also show that, experimentally, it is not much worse than k-means++ while operating in a strictly more constrained computational model.

no code implementations • NeurIPS 2013 • Dimitris Achlioptas, Zohar Karnin, Edo Liberty

We consider the problem of selecting non-zero entries of a matrix $A$ in order to produce a sparse sketch of it, $B$, that minimizes $\|A-B\|_2$.

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