Dropout Induced Noise for Co-Creative GAN Systems

This paper demonstrates how Dropout can be used in Generative Adversarial Networks to generate multiple different outputs to one input. This method is thought as an alternative to latent space exploration, especially if constraints in the input should be preserved, like in A-to-B translation tasks

Artificial Development by Reinforcement Learning Can Benefit From Multiple Motivations

Research on artificial development, reinforcement learning, and intrinsic motivations like curiosity could profit from the recently developed framework of multi-objective reinforcement learning. The combination of these ideas may lead to more realistic artificial models for life-long learning and goal directed behavior in animals and humans

Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability

Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning applications. However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventional convolutional neural networks (CNNs) due to their lack of adaptability with modern architectures. In this paper, we adapt RBF networks as a classifier on top of CNNs by modifying the training process and introducing a new activation function to train modern vision architectures end-to-end for image classification. The specific architecture of RBFs enables the learning of a similarity distance metric to compare and find similar and dissimilar images. Furthermore, we demonstrate that using an RBF classifier on top of any CNN architecture provides new human-interpretable insights about the decision-making process of the models. Finally, we successfully apply RBFs to a range of CNN architectures and evaluate the results on benchmark computer vision datasets

A dataset of continuous affect annotations and physiological signals for emotion analysis

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.Comment: Dataset available at: https://rmc.dlr.de/download/CASE_dataset/CASE_dataset.zi

Potential analysis of a Quantum RL controller in the context of autonomous driving

The potential of quantum enhanced Q-learning with a focus on its applicability to a lane change manoeuvre is investigated. In this context we solve multiple simple reinforcement learning environments using variational quantum circuits. The achieved results were similar to or even better than those of a simple constrained classical agent. We could observe promising behaviour on the more complex lane change manoeuvre task, which has an environment with an observation vector size twice larger than commonly used ones. For the Frozen Lake environment we found indications of possible quantum advantages in convergence rate

Two to trust : AutoML for safe modelling and interpretable deep learning for robustness

With great power comes great responsibility. The success of machine learning, especially deep learning, in research and practice has attracted a great deal of interest, which in turn necessitates increased trust. Sources of mistrust include matters of model genesis ("Is this really the appropriate model?") and interpretability ("Why did the model come to this conclusion?", "Is the model safe from being easily fooled by adversaries?"). In this paper, two partners for the trustworthiness tango are presented: recent advances and ideas, as well as practical applications in industry in (a) Automated machine learning (AutoML), a powerful tool to optimize deep neural network architectures and netune hyperparameters, which promises to build models in a safer and more comprehensive way; (b) Interpretability of neural network outputs, which addresses the vital question regarding the reasoning behind model predictions and provides insights to improve robustness against adversarial attacks

PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis

With the spread of COVID-19 over the world, the need arose for fast and precise automatic triage mechanisms to decelerate the spread of the disease by reducing human efforts e.g. for image-based diagnosis. Although the literature has shown promising efforts in this direction, reported results do not consider the variability of CT scans acquired under varying circumstances, thus rendering resulting models unfit for use on data acquired using e.g. different scanner technologies. While COVID-19 diagnosis can now be done efficiently using PCR tests, this use case exemplifies the need for a methodology to overcome data variability issues in order to make medical image analysis models more widely applicable. In this paper, we explicitly address the variability issue using the example of COVID-19 diagnosis and propose a novel generative approach that aims at erasing the differences induced by e.g. the imaging technology while simultaneously introducing minimal changes to the CT scans through leveraging the idea of deep autoencoders. The proposed prepossessing architecture (PrepNet) (i) is jointly trained on multiple CT scan datasets and (ii) is capable of extracting improved discriminative features for improved diagnosis. Experimental results on three public datasets (SARS-COVID-2, UCSD COVID-CT, MosMed) show that our model improves cross-dataset generalization by up to 11:84 percentage points despite a minor drop in within dataset performance

Integral representation of normalized weak Markov systems

AbstractA necessary and sufficient condition for the existence of an integral representation of weak Markov systems is given. This theorem generalizes results of Zalik and Zielke. The proof is based on the relative differentiation method of weak Markov systems introduced by Zielke, and on new alternation and oscillation properties of weak M+ systems, which may be of some independent interest

Ordinal classification: working definition and detection of ordinal structures

Ordinal classification (OC) is an important niche of supervised pattern recognition, in which the classes constitute an ordinal structure. In general, the ordinal structure can be identified, either according to the natural occurrence of the current task (e.g. healthy - mild condition - moderate condition - severe condition), or by extracting expert knowledge. However, we assume that many multi-class classification tasks might have a hidden ordinal structure, which, once identified, can facilitate and hence leverage the classification process. Therefore, we propose a working definition for OC tasks, which is based on the decision boundaries of standard binary Support Vector Machines. Moreover, resulting from our proposed definition, we introduce a simple algorithm for the detection of ordinal structures. Our proposed definition is easy to interpret and reflects an intuitive understanding of ordinal structures. Another main advantage is that our proposed definition is easy to apply. Therefore, there is no more dependence on expert knowledge for the identification of (non-intuitive) ordinal class structures. In the current study, we include ten benchmark data sets from the field of OC to experimentally evaluate and hence to confirm the validity of our proposed definition. Additionally, we analyse our proposed definition based on a small set of traditionally non-ordinal multi-class classification tasks. Furthermore, we provide an analysis of the computational cost of our proposed detection algorithm, and discuss the limitations of our proposed working definition