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Found 9 papers, 2 papers with code
Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network
We experimentally demonstrate a novel, low-complexity Fourier Convolution-based Network (FConvNet) based equalizer for 112 Gb/s upstream PAM4-PON.
A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON
A frequency-calibrated SCINet (FC-SCINet) equalizer is proposed for down-stream 100G PON with 28. 7 dB path loss.
56 GBaud PAM-4 100 km Transmission System with Photonic Processing Schemes
In this work, we show that the effectiveness of the internal fading memory depends significantly on the properties of the signal to be processed.
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably
To the best of our knowledge, this is the first work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model.
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i. e.,} $d\gg n$) where the underlying parameter is assumed to be sparse.
Consistent $k$-Median: Simpler, Better and Robust
In this paper we introduce and study the online consistent $k$-clustering with outliers problem, generalizing the non-outlier version of the problem studied in [Lattanzi-Vassilvitskii, ICML17].
Facility Location Problem in Differential Privacy Model Revisited
On the negative side, we show that the approximation ratio of any $\epsilon$-DP algorithm is lower bounded by $\Omega(\frac{1}{\sqrt{\epsilon}})$, even for instances on HST metrics with uniform facility cost, under the super-set output setting.
Distributed k-Clustering for Data with Heavy Noise
In this paper, we improve the number of outliers to the best possible $(1+\epsilon)z$, while maintaining the $O(1)$-approximation ratio and independence of communication cost on $z$.
Distributed $k$-Clustering for Data with Heavy Noise
In this paper, we improve the number of outliers to the best possible $(1+\epsilon)z$, while maintaining the $O(1)$-approximation ratio and independence of communication cost on $z$.
