removed old resarch
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layout: single
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title: "Trajectory annotation by spatial perception"
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categories: research
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excerpt: "We propose an approach to annotate trajectories using sequences of spatial perception."
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header:
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teaser: assets/figures/0_trajectory_reconstruction_teaser.png
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---
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In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities. In order to simplify the verbal communication and the interaction between robotic units and/or humans, reliable and robust systems w.r.t. noise and processing results are needed. This work builds a foundation to address this task. By using a continuous representation of spatial perception in interiors learned from trajectory data, our approach clusters movement in dependency to its spatial context. We propose an unsupervised learning approach based on a neural autoencoding that learns semantically meaningful continuous encodings of spatio-temporal trajectory data. This learned encoding can be used to form prototypical representations. We present promising results that clear the path for future applications. {% cite feld2018trajectory %}
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title: "Self-Replication in Neural Networks"
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categories: research
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excerpt: "Introduction of NNs that are able to replicate their own weights."
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header:
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teaser: assets/figures/1_self_replication_pca_space.jpg
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---
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{:style="display:block; margin-left:auto; margin-right:auto; width:350px"}
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The foundation of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. We argue that backpropagation is the natural way to navigate the space of network weights and show how it allows non-trivial self-replicators to arise naturally. We then extend the setting to construct an artificial chemistry environment of several neural networks.
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{% cite gabor2019self %}
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layout: single
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title: "Deep-Neural Baseline"
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categories: research
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excerpt: "Introduction a deep baseline for audio classification."
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header:
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teaser: assets/figures/3_deep_neural_baselines_teaser.jpg
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---
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Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE). We propose an end-to-end deep learning approach to detect and classify patterns reflecting sleepiness in the human voice. Our approach is based solely on a moderately complex deep neural network architecture. It may be applied directly on the audio data without requiring any specific feature engineering, thus remaining transferable to other audio classification tasks. Nevertheless, our approach performs similar to state-of-the-art machine learning models.
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{% cite elsner2019deep %}
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---
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layout: single
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title: "Point Cloud Segmentation"
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categories: research
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excerpt: "Segmetation of point clouds into primitive building blocks."
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header:
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teaser: assets/figures/4_point_cloud_segmentation_teaser.jpg
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---
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The segmentation and fitting of solid primitives to 3D point clouds is a complex task. Existing systems are restricted either in the number of input points or the supported primitive types. This paper proposes a hybrid pipeline that is able to reconstruct spheres, bounded cylinders and rectangular cuboids on large point sets. It uses a combination of deep learning and classical RANSAC for primitive fitting, a DBSCAN-based clustering scheme for increased stability and a specialized Genetic Algorithm for robust cuboid extraction. In a detailed evaluation, its performance metrics are discussed and resulting solid primitive sets are visualized. The paper concludes with a discussion of the approach’s limitations.
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{% cite friedrich2020hybrid %}
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---
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layout: single
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title: "Policy Entropy for OOD Classification"
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categories: research
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excerpt: "PEOC for reliably detecting unencountered states in deep RL"
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header:
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teaser: assets/figures/6_ood_pipeline.jpg
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---
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One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent's policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.
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{% cite sedlmeier2020peoc %}
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---
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layout: single
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title: "What to do in the Meantime"
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categories: research
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excerpt: "Service Coverage Analysis for Parked Autonomous Vehicles"
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header:
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teaser: assets/figures/5_meantime_coverage.jpg
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---
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Fully autonomously driving vehicles are expected to be a widely available technology in the near future. Privately owned cars, which remain parked for the majority of their lifetime, may therefore be capable of driving independently during their usual long parking periods (e.g. their owners working hours). Our analysis aims to focus on the potential of a privately owned shared car concept as transition period between the present usages of privately owned cars towards a transportation paradigm of privately owned shared autonomous vehicles. We propose two methods in the field of reachability analysis to evaluate the impact of such vehicles during parking periods. Our proposed methods are applied to a dataset of parking times of users of a telematics service provider in the Munich area (Germany). We show the impact of time and location dependent effects on the analyzed service coverage, such as business week rush hours or cover age divergence between urban and suburban regions.
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{% cite illium2020meantime %}
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---
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layout: single
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title: "Surgical Mask Detection"
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categories: research audio deep-learning
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excerpt: "Convolutional Neural Networks and Data Augmentations on Spectrograms"
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header:
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teaser: assets/figures/7_mask_models.jpg
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---
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In many fields of research, labeled data-sets are hard to acquire. This is where data augmentation promises to overcome the lack of training data in the context of neural network engineering and classification tasks. The idea here is to reduce model over-fitting to the feature distribution of a small under-descriptive training data-set. We try to evaluate such data augmentation techniques to gather insights in the performance boost they provide for several convolutional neural networks on mel-spectrogram representations of audio data. We show the impact of data augmentation on the binary classification task of surgical mask detection in samples of human voice. Also we consider four varying architectures to account for augmentation robustness. Results show that most of the baselines given by ComParE are outperformed
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{% cite illium2020surgical %}
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---
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layout: single
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title: "Anomalous Sound Detection"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Analysis of Feature Representations for Anomalous Sound Detection"
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header:
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teaser: assets/figures/8_anomalous_sound_teaser.jpg
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---
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{:style="display:block; margin-left:auto; margin-right:auto"}
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The problem of Constructive Solid Geometry (CSG) tree reconstruction from 3D point clouds or 3D triangle meshes is hard to solve. At first, the input data set (point cloud, triangle soup or triangle mesh) has to be segmented and geometric primitives (spheres, cylinders, ...) have to be fitted to each subset. Then, the size- and shape optimal CSG tree has to be extracted. We propose a pipeline for CSG reconstruction consisting of multiple stages: A primitive extraction step, which uses deep learning for primitive detection, a clustered variant of RANSAC for parameter fitting, and a Genetic Algorithm (GA) for convex polytope generation. It directly transforms 3D point clouds or triangle meshes into solid primitives. The filtered primitive set is then used as input for a GA-based CSG extraction stage. We evaluate two different CSG extraction methodologies and furthermore compare our pipeline to current state-of-the-art methods.
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{% cite muller2020analysis %}
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---
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layout: single
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title: "Anomalous Image Transfer"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning"
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header:
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teaser: assets/figures/9_image_transfer_sound_teaser.jpg
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---
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{:style="display:block; margin-left:auto; margin-right:auto"}
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In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pretrained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to convolutional autoencoders in recordings of four different factory machines in noisy environments. Moreover, we find that features extracted from ResNet based networks yield better results than those from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and One-Class Support Vector Machines achieve the best anomaly detection performance.
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{% cite muller2020acoustic %}
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---
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layout: single
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title: "Acoustic Leak Detection"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Anomalie based Leak Detection in Water Networks"
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header:
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teaser: assets/figures/10_water_networks_teaser.jpg
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---
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{:style="display:block; margin-left:auto; margin-right:auto"}
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In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pretrained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to convolutional autoencoders in recordings of four different factory machines in noisy environments. Moreover, we find that features extracted from ResNet based networks yield better results than those from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and One-Class Support Vector Machines achieve the best anomaly detection performance.
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{% cite muller2021acoustic %}
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---
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layout: single
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title: "Primate Vocalization Classification"
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categories: research audio deep-learning anomalie-detection
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excerpt: "A Deep and Recurrent Architecture"
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header:
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teaser: assets/figures/11_recurrent_primate_workflow.jpg
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---
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Wildlife monitoring is an essential part of most conservation efforts where one of the many building blocks is acoustic monitoring. Acoustic monitoring has the advantage of being noninvasive and applicable in areas of high vegetation. In this work, we present a deep and recurrent architecture for the classification of primate vocalizations that is based upon well proven modules such as bidirectional Long Short-Term Memory neural networks, pooling, normalized softmax and focal loss. Additionally, we apply Bayesian optimization to obtain a suitable set of hyperparameters. We test our approach on a recently published dataset of primate vocalizations that were recorded in an African wildlife sanctuary.
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{% cite muller2021deep %}
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---
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layout: single
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title: "Mel-Vision Transformer"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Attention based audio classification on Mel-Spektrograms"
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header:
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teaser: assets/figures/12_vision_transformer_teaser.jpg
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---
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We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and sample-weighting, we achieve comparable performance on both (PRS and CCS challenge) tasks of ComParE21, outperforming most single model baselines. We further introduce overlapping vertical patching and evaluate the influence of parameter configurations.
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{% cite illium2021visual %}
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---
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layout: single
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title: "Self-Replication Goals"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Combining replication and auxiliary task for neural networks."
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header:
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teaser: assets/figures/13_sr_teaser.jpg
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---
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Self-replicating neural networks can be trained to output a representation of themselves, making them navigate towards non-trivial fixpoints in their weight space. We explore the problem of adding a secondary functionality to the primary task of replication. We find a successful solution in training the networks with separate input/output vectors for one network trained in both tasks so that the additional task does not hinder (and even stabilizes) the self-replication task. Furthermore, we observe the interaction of our goal-networks in an artificial chemistry environment. We examine the influence of different action parameters on the population and their effects on the group’s learning capability. Lastly we show the possibility of safely guiding the whole group to goal-fulfilling weight configurations via the inclusion of one specially-developed guiding particle that is able to propagate a secondary task to its peers.
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{% cite gabor2021goals %}
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---
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layout: single
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title: "Anomaly Detection in RL"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Towards Anomaly Detection in Reinforcement Learning"
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header:
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teaser: assets/figures/14_ad_rl_teaser.jpg
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---
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Identifying datapoints that substantially differ from normality is the task of anomaly detection (AD). While AD has gained widespread attention in rich data domains such as images, videos, audio and text, it has has been studied less frequently in the context of reinforcement learning (RL). This is due to the additional layer of complexity that RL introduces through sequential decision making. Developing suitable anomaly detectors for RL is of particular importance in safety-critical scenarios where acting on anomalous data could result in hazardous situations. In this work, we address the question of what AD means in the context of RL. We found that current research trains and evaluates on overly simplistic and unrealistic scenarios which reduce to classic pattern recognition tasks. We link AD in RL to various fields in RL such as lifelong RL and generalization. We discuss their similarities, differences, and how the fields can benefit from each other. Moreover, we identify non-stationarity to be one of the key drivers for future research on AD in RL and make a first step towards a more formal treatment of the problem by framing it in terms of the recently introduced block contextual Markov decision process. Finally, we define a list of practical desiderata for future problems.
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{% cite muller2022towards %}
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---
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layout: single
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title: "Self-Replication in NNs"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Elaboration and journal article of the initial paper"
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header:
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teaser: assets/figures/15_sr_journal_teaser.jpg
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---
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{:style="display:block; margin-left:auto; margin-right:auto"}
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A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this works previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.
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{% cite gabor2022self %}
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---
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layout: single
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title: "Organism Networks"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Constructing ON from Collaborative Self-Replicators"
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header:
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teaser: assets/figures/16_on_teaser.jpg
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---
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{:style="display:block; margin-left:auto; margin-right:auto"}
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A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this works previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.
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{% cite illium2022constructing %}
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---
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layout: single
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title: "Voronoi Patches"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Evaluating A New Data Augmentation Method"
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header:
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teaser: assets/figures/17_vp_teaser.jpg
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---
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Overfitting is a problem in Convolutional Neural Networks (CNN) that causes poor generalization of models on unseen data. To remediate this problem, many new and diverse data augmentation methods (DA) have been proposed to supplement or generate more training data, and thereby increase its quality. In this work, we propose a new data augmentation algorithm: VoronoiPatches (VP). We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches. Unlike other DA methods, VP uses small convex polygon-shaped patches in a random layout to transport information around within an image. Sudden transitions created between patches and the original image can, optionally, be smoothed. In our experiments, VP outperformed current DA methods regarding model variance and overfitting tendencies. We demonstrate data augmentation utilizing non-linear re-combination of information within images, and non-orthogonal shapes and structures improves CNN model robustness on unseen data.
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{% cite illium2022voronoipatches %}
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:trophy: This paper won the conference's [Best Poster Award](https://icaart.scitevents.org/PreviousAwards.aspx?y=2024#2023), which is a special honor. :trophy:
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---
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layout: single
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title: "Social NN-Soup"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Social interaction based on surprise minimization"
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header:
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teaser: assets/figures/18_surprised_soup_teaser.jpg
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---
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{: .align-right style="padding:2em; width:20em"}
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A recent branch of research in artificial life has constructed artificial chemistry systems whose particles are dynamic neural networks. These particles can be applied to each other and show a tendency towards self-replication of their weight values. We define new interactions for said particles that allow them to recognize one another and learn predictors for each other’s behavior. For instance, each particle minimizes its surprise when observing another particle’s behavior. Given a special catalyst particle to exert evolutionary selection pressure on the soup of particles, these ‘social’ interactions are sufficient to produce emergent behavior similar to the stability pattern previously only achieved via explicit self-replication training.
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{% cite zorn23surprise %}
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{:style="display:block; margin-left:auto; margin-right:auto;"}
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---
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layout: single
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title: "Binary Presorting"
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categories: research audio deep-learning anomalie-detection
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excerpt: "Improving Primate Sounds Classification"
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header:
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teaser: assets/figures/19_binary_primates_teaser.jpg
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---
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{:style="display:block; margin-left:auto; margin-right:auto"}
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In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available datasets from this field of research are often not optimal learning material; Samples can be weakly labeled, of different lengths or come with a poor signal-to-noise ratio. In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations, to achieve higher performances on the actual multi-class classification tasks. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging ComparE 2021 dataset, with the task of classifying between different primate species sounds, and report significantly higher Accuracy and UAR scores in contrast to comparatively equipped model baselines.
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{% cite koelle23primate %}
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---
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layout: single
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title: "Seminar: TIMS"
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categories: teaching
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excerpt: "Teaching bachelor students how to work scientifically and how to do research as a team."
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header:
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teaser: assets/images/teaching/thesis.png
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---
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{: .align-left style="padding:0.1em; width:5em"}
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This seminar deals with selected topics from the field of mobile and distributed systems, in particular from the main research areas of the chair. In recent semesters, this has led to a focus on topics from the field of machine learning and quantum computing.
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### Content
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<div class="align-right">
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| Summer semester | Winter semester |
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| --- | --- |
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| [2023](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-sose23/)| --- |
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||||
| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-sose22/)| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2122-2/) |
|
||||
| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-sose21/)| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2122-2/) |
|
||||
| --- |[2020](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-wise2021/)|
|
||||
|
||||
</div>One aim of the seminar is also to learn and practise scientific working techniques. To this end, a course on presentation and working techniques is offered during the semester and supplemented by individual presentation coaching/feedback.
|
||||
|
||||
The final grade for the seminar is based on the quality of the academic work, the presentation and active participation in the seminars.
|
||||
@ -1,26 +0,0 @@
|
||||
---
|
||||
layout: single
|
||||
title: "Seminar: VTIMS"
|
||||
categories: teaching
|
||||
excerpt: "Teaching master students how to work scientifically and how to do research as a team."
|
||||
header:
|
||||
teaser: assets/images/teaching/thesis_master.png
|
||||
---
|
||||
|
||||
{: .align-left style="padding:0.1em; width:5em"}
|
||||
This seminar deals with selected topics from the field of mobile and distributed systems, in particular from the main research topics of the chair.
|
||||
In recent semesters, this has led to a focus on topics from the field of machine learning and quantum computing.
|
||||
|
||||
### Content
|
||||
<div class="table-right">
|
||||
|
||||
| Summer semester | Winter semester |
|
||||
| --- | --- |
|
||||
| [2023](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-sose23/)| --- |
|
||||
| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-sose22/)| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2223/) |
|
||||
| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-sose21/)| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2122/) |
|
||||
| --- |[2020](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2021/)|
|
||||
|
||||
</div>One aim of the seminar is also to learn and practise scientific working techniques. To this end, a course on presentation and working techniques is offered during the semester and supplemented by individual presentation coaching/feedback.
|
||||
|
||||
The final grade for the seminar is based on the quality of the academic work, the presentation and active participation in the seminars.
|
||||
@ -42,7 +42,7 @@
|
||||
(facebook, $facebook-color),
|
||||
(linkedin, $linkedin-color),
|
||||
(mastodon, $mastodon-color),
|
||||
(twitter, $twitter-color),
|
||||
(twitter, $twitter-color);
|
||||
|
||||
@each $buttoncolor, $color in $buttoncolors {
|
||||
&--#{$buttoncolor} {
|
||||
|
||||
Reference in New Issue
Block a user