162 citations · 471 across the 27 of their papers we have counts for
6 papers · 1 filter
Improving Reproducible Deep Learning Workflows with DeepDIVA
Michele Alberti, Vinaychandran Pondenkandath, Lars Vögtlin +3
The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results…
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Kumar Shridhar, Felix Laumann, Marcus Liwicki
Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an opt…
Leveraging Random Label Memorization for Unsupervised Pre-Training
Vinaychandran Pondenkandath, Michele Alberti, Sammer Puran +2
We present a novel approach to leverage large unlabeled datasets by pre-training state-of-the-art deep neural networks on randomly-labeled datasets. Specifically, we train the neur…
Are You Tampering With My Data?
Michele Alberti, Vinaychandran Pondenkandath, Marcel Würsch +4
We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. O…
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference
Kumar Shridhar, Felix Laumann, Marcus Liwicki
We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus…
Bidirectional Learning for Robust Neural Networks
Sidney Pontes-Filho, Marcus Liwicki
A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL). It consists of training an undirected neural network to map input to output a…