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Found 9 papers, 1 papers with code
Improved identification accuracy in equation learning via comprehensive $\boldsymbol{R^2}$-elimination and Bayesian model selection
With two flavors of our approach and the adoption of $p(\boldsymbol y|\mathcal M)$ for bi-directional stepwise regression, we present a total of three new avenues for equation learning.
A physics-informed neural network framework for modeling obstacle-related equations
Deep learning has been highly successful in some applications.
Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and computational demands.
Towards the Localisation of Lesions in Diabetic Retinopathy
Convolutional Neural Networks (CNNs) have successfully been used to classify diabetic retinopathy (DR) fundus images in recent times.
An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions
We study deep neural networks with binary activation functions (BDNN), i. e. the activation function only has two states.
On Error Correction Neural Networks for Economic Forecasting
Recurrent neural networks (RNNs) are more suitable for learning non-linear dependencies in dynamical systems from observed time series data.
Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
We study the convergence of gradient flows related to learning deep linear neural networks (where the activation function is the identity map) from data.
Convex block-sparse linear regression with expanders -- provably
Our experimental findings on synthetic and real applications support our claims for faster recovery in the convex setting -- as opposed to using dense sensing matrices, while showing a competitive recovery performance.
Energy-aware adaptive bi-Lipschitz embeddings
We propose a dimensionality reducing matrix design based on training data with constraints on its Frobenius norm and number of rows.
