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04/07/2023
A physics-informed neural network framework for modeling obstacle-related equations
Deep learning has been highly successful in some applications. Neverthel...
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10/21/2021
Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
Compared to classical deep neural networks its binarized versions can be...
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12/21/2020
Towards the Localisation of Lesions in Diabetic Retinopathy
Convolutional Neural Networks (CNN) has successfully been used to classi...
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07/17/2020
Practical High-Throughput, Non-Adaptive and Noise-Robust SARS-CoV-2 Testing
We propose a compressed sensing-based testing approach with a practical ...
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07/07/2020
An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions
We study deep neural networks with binary activation functions (BDNN), i...
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04/11/2020
On Error Correction Neural Networks for Economic Forecasting
Recurrent neural networks (RNNs) are more suitable for learning non-line...
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03/29/2020
Efficient Noise-Blind ℓ_1-Regression of Nonnegative Compressible Signals
In compressed sensing the goal is to recover a signal from as few as pos...
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10/12/2019
Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
We study the convergence of gradient flows related to learning deep line...
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04/02/2019
Discrete Optimization Methods for Group Model Selection in Compressed Sensing
In this article we study the problem of signal recovery for group models...
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04/24/2018
On the construction of sparse matrices from expander graphs
We revisit the asymptotic analysis of probabilistic construction of adja...
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03/21/2016