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Found 7 papers, 1 papers with code
Model-free readout-error mitigation for quantum expectation values
Here, we propose a very simple method that forces the bias in the expectation value to appear as a multiplicative factor that can be measured directly and removed at the cost of an increase in the sampling complexity for the observable.
Quantum Physics
Estimating Information Flow in DNNs
We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models.
The Ocean Tensor Package
Matrix and tensor operations form the basis of a wide range of fields and applications, and in many cases constitute a substantial part of the overall computational complexity.
Estimating Information Flow in Deep Neural Networks
We then develop a rigorous estimator for $I(X;T)$ in noisy DNNs and observe compression in various models.
Training variance and performance evaluation of neural networks in speech
In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Some Insights into the Geometry and Training of Neural Networks
Neural networks have been successfully used for classification tasks in a rapidly growing number of practical applications.
SLOPE - Adaptive variable selection via convex optimization
SLOPE, short for Sorted L-One Penalized Estimation, is the solution to \[\min_{b\in\mathbb{R}^p}\frac{1}{2}\Vert y-Xb\Vert _{\ell_2}^2+\lambda_1\vert b\vert _{(1)}+\lambda_2\vert b\vert_{(2)}+\cdots+\lambda_p\vert b\vert_{(p)},\] where $\lambda_1\ge\lambda_2\ge\cdots\ge\lambda_p\ge0$ and $\vert b\vert_{(1)}\ge\vert b\vert_{(2)}\ge\cdots\ge\vert b\vert_{(p)}$ are the decreasing absolute values of the entries of $b$.
Methodology
