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Found 9 papers, 1 papers with code
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.
Isoform Function Prediction Using a Deep Neural Network
Most studies have shown that alternative splicing plays a significant role in human health and disease.
Distributed Sparse Feature Selection in Communication-Restricted Networks
This paper aims to propose and theoretically analyze a new distributed scheme for sparse linear regression and feature selection.
Regularizing Recurrent Neural Networks via Sequence Mixup
In this paper, we extend a class of celebrated regularization techniques originally proposed for feed-forward neural networks, namely Input Mixup (Zhang et al., 2017) and Manifold Mixup (Verma et al., 2018), to the realm of Recurrent Neural Networks (RNN).
Structure Learning of Sparse GGMs over Multiple Access Networks
In Uncoded method, data symbols are scaled and transmitted through the channel.
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.
Learning of Tree-Structured Gaussian Graphical Models on Distributed Data under Communication Constraints
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed.
Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks
Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes.
Learning of Gaussian Processes in Distributed and Communication Limited Systems
In particular, it is shown that the performance of one of the practical schemes which is called per-symbol quantization is very close to the optimal one.
