8 citations · 8 across the 2 of their papers we have counts for
8 papers
Towards the Localisation of Lesions in Diabetic Retinopathy
Samuel Ofosu Mensah, Bubacarr Bah, Willie Brink
Convolutional Neural Networks (CNNs) have successfully been used to classify diabetic retinopathy (DR) fundus images in recent times. However, deeper representations in CNNs may ca…
An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions
Bubacarr Bah, Jannis Kurtz
We study deep neural networks with binary activation functions (BDNN), i.e. the activation function only has two states. We show that the BDNN can be reformulated as a mixed-intege…
On Error Correction Neural Networks for Economic Forecasting
Mhlasakululeka Mvubu, Emmanuel Kabuga, Christian Plitz +3
Recurrent neural networks (RNNs) are more suitable for learning non-linear dependencies in dynamical systems from observed time series data. In practice all the external variables…
Efficient Tuning-Free -Regression of Nonnegative Compressible Signals
Hendrik Bernd Petersen, Bubacarr Bah, Peter Jung
In compressed sensing the goal is to recover a signal from as few as possible noisy, linear measurements. The general assumption is that the signal has only a few non-zero entries.…
Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
Bubacarr Bah, Holger Rauhut, Ulrich Terstiege +1
We study the convergence of gradient flows related to learning deep linear neural networks (where the activation function is the identity map) from data. In this case, the composit…
Discrete Optimization Methods for Group Model Selection in Compressed Sensing
Bubacarr Bah, Jannis Kurtz, Oliver Schaudt
In this article we study the problem of signal recovery for group models. More precisely for a given set of groups, each containing a small subset of indices, and for given linear…