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Coordinated Deep Neural Networks: A Versatile Edge Offloading Algorithm
Although DNN models deployed at the edge are promising to provide AI as a service with low latency, their cooperation is yet to be explored.
Out-Of-Domain Unlabeled Data Improves Generalization
From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in $\mathbb{R}^d$, where in addition to the $m$ independent and labeled samples from the true distribution, a set of $n$ (usually with $n\gg m$) out of domain and unlabeled samples are given as well.
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
Also, we theoretically show that in order to achieve this bound, it is sufficient to have $n\ge\left(K^2/\varepsilon^2\right)e^{\Omega\left(K/\mathrm{SNR}^2\right)}$ samples, where $\mathrm{SNR}$ stands for the signal-to-noise ratio.
On Statistical Learning of Simplices: Unmixing Problem Revisited
We study the sample complexity of learning a high-dimensional simplex from a set of points uniformly sampled from its interior.
