Search Results for author: Barbara Hammer

Found 95 papers, 55 papers with code

Analyzing the Influence of Training Samples on Explanations

no code implementations5 Jun 2024 André Artelt, Barbara Hammer

However, in cases such as unexpected explanations, the user might be interested in learning about the cause of this explanation -- e. g. properties of the utilized training data that are responsible for the observed explanation.

counterfactual Counterfactual Explanation +2

A Toolbox for Supporting Research on AI in Water Distribution Networks

no code implementations4 Jun 2024 André Artelt, Marios S. Kyriakou, Stelios G. Vrachimis, Demetrios G. Eliades, Barbara Hammer, Marios M. Polycarpou

Drinking water is a vital resource for humanity, and thus, Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies.

Event Detection

KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions

no code implementations17 May 2024 Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer

As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, and demonstrate state-of-the-art performance for feature interactions.

Automated Federated Learning via Informed Pruning

1 code implementation16 May 2024 Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer

However, its application on edge devices is hindered by limited computational capabilities and data communication challenges, compounded by the inherent complexity of Deep Learning (DL) models.

Federated Learning

Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection

no code implementations10 May 2024 Thorben Markmann, Michiel Straat, Barbara Hammer

We conjecture that this is due to the LRAN's flexibility in learning complicated observables from data, thereby serving as a viable surrogate model for the main structure of fluid dynamics in turbulent convection settings.

FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation

1 code implementation12 Apr 2024 Riza Velioglu, Robin Chan, Barbara Hammer

In the realm of fashion object detection and segmentation for online shopping images, existing state-of-the-art fashion parsing models encounter limitations, particularly when exposed to non-model-worn apparel and close-up shots.

Data Augmentation Object Detection +1

Physics-Informed Graph Neural Networks for Water Distribution Systems

1 code implementation27 Mar 2024 Inaam Ashraf, Janine Strotherm, Luca Hermes, Barbara Hammer

In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS.

Faster Convergence for Transformer Fine-tuning with Line Search Methods

1 code implementation27 Mar 2024 Philip Kenneweg, Leonardo Galli, Tristan Kenneweg, Barbara Hammer

Recent works have shown that line search methods greatly increase performance of traditional stochastic gradient descent methods on a variety of datasets and architectures [1], [2].

Improving Line Search Methods for Large Scale Neural Network Training

1 code implementation27 Mar 2024 Philip Kenneweg, Tristan Kenneweg, Barbara Hammer

In recent studies, line search methods have shown significant improvements in the performance of traditional stochastic gradient descent techniques, eliminating the need for a specific learning rate schedule.

Neural Architecture Search for Sentence Classification with BERT

1 code implementation27 Mar 2024 Philip Kenneweg, Sarah Schröder, Barbara Hammer

Pre training of language models on large text corpora is common practice in Natural Language Processing.

Classification Neural Architecture Search +2

Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers

1 code implementation27 Mar 2024 Philip Kenneweg, Alexander Schulz, Sarah Schröder, Barbara Hammer

We combine the learning rate distributions thus found and show that they generalize to better performance with respect to the problem of catastrophic forgetting.

Hyperparameter Optimization

Debiasing Sentence Embedders through Contrastive Word Pairs

1 code implementation27 Mar 2024 Philip Kenneweg, Sarah Schröder, Alexander Schulz, Barbara Hammer

It is problematic that most debiasing approaches are directly transferred from word embeddings, therefore these approaches fail to take into account the nonlinear nature of sentence embedders and the embeddings they produce.

Sentence Sentence Embeddings +1

Targeted Visualization of the Backbone of Encoder LLMs

1 code implementation26 Mar 2024 Isaac Roberts, Alexander Schulz, Luca Hermes, Barbara Hammer

Attention based Large Language Models (LLMs) are the state-of-the-art in natural language processing (NLP).

Dimensionality Reduction Image Classification

Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent Setup

1 code implementation26 Feb 2024 Tristan Kenneweg, Philip Kenneweg, Barbara Hammer

We use a dataset created this way for the development and evaluation of a boolean agent RAG setup: A system in which a LLM can decide whether to query a vector database or not, thus saving tokens on questions that can be answered with internal knowledge.

Language Modelling Large Language Model +1

Semantic Properties of cosine based bias scores for word embeddings

no code implementations27 Jan 2024 Sarah Schröder, Alexander Schulz, Fabian Hinder, Barbara Hammer

Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements.

Word Embeddings

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles

1 code implementation22 Jan 2024 Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks

1 code implementation3 Jan 2024 Valerie Vaquet, Fabian Hinder, Barbara Hammer

In this work, we explore the potential of model-loss-based and distribution-based drift detection methods to tackle leakage detection.

A Remark on Concept Drift for Dependent Data

1 code implementation15 Dec 2023 Fabian Hinder, Valerie Vaquet, Barbara Hammer

Concept drift, i. e., the change of the data generating distribution, can render machine learning models inaccurate.

Time Series

One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments

no code implementations24 Oct 2023 Fabian Hinder, Valerie Vaquet, Barbara Hammer

In addition to providing a systematic literature review, this work provides precise mathematical definitions of the considered problems and contains standardized experiments on parametric artificial datasets allowing for a direct comparison of different strategies for detection and localization.

Anomaly Detection

Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations

1 code implementation24 Oct 2023 Valerie Vaquet, Fabian Hinder, Jonas Vaquet, Kathrin Lammers, Lars Quakernack, Barbara Hammer

Facing climate change, the already limited availability of drinking water will decrease in the future rendering drinking water an increasingly scarce resource.

Anomaly Detection

Fairness in KI-Systemen

no code implementations17 Jul 2023 Janine Strotherm, Alissa Müller, Barbara Hammer, Benjamin Paaßen

We explain the main fairness definitions and strategies for achieving fairness using concrete examples and place fairness research in the European context.

Fairness

iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios

1 code implementation13 Jun 2023 Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, Eyke Hüllermeier

Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence (XAI) to understand black-box machine learning models.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Adversarial Attacks on Leakage Detectors in Water Distribution Networks

1 code implementation25 May 2023 Paul Stahlhofen, André Artelt, Luca Hermes, Barbara Hammer

Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up with a wrong prediction.

Model Based Explanations of Concept Drift

no code implementations16 Mar 2023 Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf, Barbara Hammer

To do so, we propose a methodology to reduce the explanation of concept drift to an explanation of models that are trained in a suitable way extracting relevant information regarding the drift.

iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams

no code implementations2 Mar 2023 Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

Existing methods for explainable artificial intelligence (XAI), including popular feature importance measures such as SAGE, are mostly restricted to the batch learning scenario.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2

Combining self-labeling and demand based active learning for non-stationary data streams

no code implementations8 Feb 2023 Valerie Vaquet, Fabian Hinder, Johannes Brinkrolf, Barbara Hammer

Learning from non-stationary data streams is a research direction that gains increasing interest as more data in form of streams becomes available, for example from social media, smartphones, or industrial process monitoring.

Active Learning valid

On the Change of Decision Boundaries and Loss in Learning with Concept Drift

no code implementations2 Dec 2022 Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf, Barbara Hammer

More precisely, we relate a change of the ITTE to the presence of real drift, i. e., a changed posterior, and to a change of the training result under the assumption of optimality.

Explainable Artificial Intelligence for Improved Modeling of Processes

1 code implementation1 Dec 2022 Riza Velioglu, Jan Philip Göpfert, André Artelt, Barbara Hammer

On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations

1 code implementation27 Nov 2022 André Artelt, Barbara Hammer

Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user.

counterfactual Decision Making +1

Unsupervised Unlearning of Concept Drift with Autoencoders

1 code implementation23 Nov 2022 André Artelt, Kleanthis Malialis, Christos Panayiotou, Marios Polycarpou, Barbara Hammer

Consequently, learning models operating on the data stream might become obsolete, and need costly and difficult adjustments such as retraining or adaptation.

Incremental Learning

Spatial Graph Convolution Neural Networks for Water Distribution Systems

1 code implementation17 Nov 2022 Inaam Ashraf, Luca Hermes, André Artelt, Barbara Hammer

We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure.

"Even if ..." -- Diverse Semifactual Explanations of Reject

2 code implementations5 Jul 2022 André Artelt, Barbara Hammer

In this work, we propose to explain rejects by semifactual explanations, an instance of example-based explanation methods, which them self have not been widely considered in the XAI community yet.

BIG-bench Machine Learning Conformal Prediction +2

Model Agnostic Local Explanations of Reject

1 code implementation16 May 2022 André Artelt, Roel Visser, Barbara Hammer

The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions.

counterfactual Decision Making

Precise Change Point Detection using Spectral Drift Detection

no code implementations13 May 2022 Fabian Hinder, André Artelt, Valerie Vaquet, Barbara Hammer

The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and need adjustment.

Change Point Detection

Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

1 code implementation11 May 2022 Ulrike Kuhl, André Artelt, Barbara Hammer

Following the view of psychological plausibility as comparative similarity, this may be explained by the fact that users in the closest condition experience their CFEs as more psychologically plausible than the computationally plausible counterpart.

counterfactual Experimental Design +2

Stream-based Active Learning with Verification Latency in Non-stationary Environments

1 code implementation14 Apr 2022 Andrea Castellani, Sebastian Schmitt, Barbara Hammer

Furthermore, we propose a drift-dependent dynamic budget strategy, which uses a variable distribution of the labelling budget over time, after a detected drift.

Active Learning

SAM-kNN Regressor for Online Learning in Water Distribution Networks

1 code implementation4 Apr 2022 Jonathan Jakob, André Artelt, Martina Hasenjäger, Barbara Hammer

In this work, we propose an adaption of the incremental SAM-kNN classifier for regression to build a residual based anomaly detection system for water distribution networks that is able to adapt to any kind of change.

Anomaly Detection

The SAME score: Improved cosine based bias score for word embeddings

no code implementations28 Mar 2022 Sarah Schröder, Alexander Schulz, Philip Kenneweg, Robert Feldhans, Fabian Hinder, Barbara Hammer

Furthermore, we thoroughly investigate the existing cosine-based scores and their limitations in order to show why these scores fail to report biases in some situations.

Sentence Sentence Embeddings +1

Suitability of Different Metric Choices for Concept Drift Detection

no code implementations19 Feb 2022 Fabian Hinder, Valerie Vaquet, Barbara Hammer

In this paper, we analyze structural properties of the drift induced signals in the context of different metrics.

Explaining Reject Options of Learning Vector Quantization Classifiers

1 code implementation15 Feb 2022 André Artelt, Johannes Brinkrolf, Roel Visser, Barbara Hammer

While machine learning models are usually assumed to always output a prediction, there also exist extensions in the form of reject options which allow the model to reject inputs where only a prediction with an unacceptably low certainty would be possible.

counterfactual Quantization

A Graph-based U-Net Model for Predicting Traffic in unseen Cities

1 code implementation11 Feb 2022 Luca Hermes, Barbara Hammer, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling

Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow.

Management Traffic Prediction

Evaluating Metrics for Bias in Word Embeddings

no code implementations15 Nov 2021 Sarah Schröder, Alexander Schulz, Philip Kenneweg, Robert Feldhans, Fabian Hinder, Barbara Hammer

However, lately some works have raised doubts about these metrics showing that even though such metrics report low biases, other tests still show biases.

Sentence Sentence Embeddings +1

Task-Sensitive Concept Drift Detector with Constraint Embedding

1 code implementation16 Aug 2021 Andrea Castellani, Sebastian Schmitt, Barbara Hammer

In the proposed framework, the actual method to detect a change in the statistics of incoming data samples can be chosen freely.

Metric Learning

Single-Step Adversarial Training for Semantic Segmentation

no code implementations30 Jun 2021 Daniel Wiens, Barbara Hammer

Finding such a step size, without increasing the computational effort of single-step adversarial training, is still an open challenge.

Segmentation Semantic Segmentation

Convex optimization for actionable \& plausible counterfactual explanations

1 code implementation17 May 2021 André Artelt, Barbara Hammer

Transparency is an essential requirement of machine learning based decision making systems that are deployed in real world.

counterfactual Decision Making

Reservoir Stack Machines

1 code implementation4 May 2021 Benjamin Paaßen, Alexander Schulz, Barbara Hammer

In this paper, we introduce the reservoir stack machine, a model which can provably recognize all deterministic context-free languages and circumvents the training problem by training only the output layer of a recurrent net and employing auxiliary information during training about the desired interaction with a stack.

Contrastive Explanations for Explaining Model Adaptations

1 code implementation6 Apr 2021 André Artelt, Fabian Hinder, Valerie Vaquet, Robert Feldhans, Barbara Hammer

We also propose a method for automatically finding regions in data space that are affected by a given model adaptation and thus should be explained.

Decision Making

Evaluating Robustness of Counterfactual Explanations

1 code implementation3 Mar 2021 André Artelt, Valerie Vaquet, Riza Velioglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling, Barbara Hammer

Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system.

counterfactual Decision Making +1

Intuitiveness in Active Teaching

no code implementations25 Dec 2020 Jan Philip Göpfert, Ulrike Kuhl, Lukas Hindemith, Heiko Wersing, Barbara Hammer

After developing a theoretical framework of intuitiveness as a property of algorithms, we introduce an active teaching paradigm involving a prototypical two-dimensional spatial learning task as a method to judge the efficacy of human-machine interactions.

BIG-bench Machine Learning

Analysis of Drifting Features

1 code implementation1 Dec 2020 Fabian Hinder, Jonathan Jakob, Barbara Hammer

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time.

feature selection

Interpretable Locally Adaptive Nearest Neighbors

no code implementations8 Nov 2020 Jan Philip Göpfert, Heiko Wersing, Barbara Hammer

When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric.

Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation

1 code implementation20 Oct 2020 Dominik Stallmann, Jan P. Göpfert, Julian Schmitz, Alexander Grünberger, Barbara Hammer

Motivation: Innovative microfluidic systems carry the promise to greatly facilitate spatio-temporal analysis of single cells under well-defined environmental conditions, allowing novel insights into population heterogeneity and opening new opportunities for fundamental and applied biotechnology.

Cell Tracking Time Series Analysis

Efficient computation of contrastive explanations

1 code implementation6 Oct 2020 André Artelt, Barbara Hammer

With the increasing deployment of machine learning systems in practice, transparency and explainability have become serious issues.

BIG-bench Machine Learning counterfactual

Reservoir Memory Machines as Neural Computers

1 code implementation14 Sep 2020 Benjamin Paaßen, Alexander Schulz, Terrence C. Stewart, Barbara Hammer

Differentiable neural computers extend artificial neural networks with an explicit memory without interference, thus enabling the model to perform classic computation tasks such as graph traversal.

Counterfactual Explanations of Concept Drift

no code implementations23 Jun 2020 Fabian Hinder, Barbara Hammer

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment.

counterfactual

Supervised Learning in the Presence of Concept Drift: A modelling framework

no code implementations21 May 2020 Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl

We present a modelling framework for the investigation of supervised learning in non-stationary environments.

Quantization

Sequential Feature Classification in the Context of Redundancies

3 code implementations1 Apr 2020 Lukas Pfannschmidt, Barbara Hammer

The problem of all-relevant feature selection is concerned with finding a relevant feature set with preserved redundancies.

Classification feature selection +1

Convex Density Constraints for Computing Plausible Counterfactual Explanations

1 code implementation12 Feb 2020 André Artelt, Barbara Hammer

The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning models.

BIG-bench Machine Learning counterfactual

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

no code implementations10 Dec 2019 Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tino, Barbara Hammer

In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model.

feature selection regression

A probability theoretic approach to drifting data in continuous time domains

1 code implementation4 Dec 2019 Fabian Hinder, André Artelt, Barbara Hammer

The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time.

Change Point Detection

On the computation of counterfactual explanations -- A survey

1 code implementation15 Nov 2019 André Artelt, Barbara Hammer

Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models.

BIG-bench Machine Learning counterfactual

Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation

no code implementations12 Nov 2019 Babak Hosseini, Romain Montagne, Barbara Hammer

Convolutional neural networks (CNNs) are deep learning frameworks which are well-known for their notable performance in classification tasks.

Action Recognition Skeleton Based Action Recognition

Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection

no code implementations10 Nov 2019 Babak Hosseini, Barbara Hammer

In this paper, we propose a novel interpretable multiple-kernel prototype learning (IMKPL) to construct highly interpretable prototypes in the feature space, which are also efficient for the discriminative representation of the data.

feature selection

Recovering Localized Adversarial Attacks

no code implementations21 Oct 2019 Jan Philip Göpfert, Heiko Wersing, Barbara Hammer

In this contribution, we focus on the capabilities of explainers for convolutional deep neural networks in an extreme situation: a setting in which humans and networks fundamentally disagree.

Image Classification

Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold

no code implementations19 Sep 2019 Babak Hosseini, Barbara Hammer

In this research, we propose the interpretable kernel DR algorithm (I-KDR) as a new algorithm which maps the data from the feature space to a lower dimensional space where the classes are more condensed with less overlapping.

Dimensionality Reduction feature selection

Efficient computation of counterfactual explanations of LVQ models

1 code implementation2 Aug 2019 André Artelt, Barbara Hammer

The increasing use of machine learning in practice and legal regulations like EU's GDPR cause the necessity to be able to explain the prediction and behavior of machine learning models.

BIG-bench Machine Learning counterfactual +2

Adversarial Robustness Curves

no code implementations31 Jul 2019 Christina Göpfert, Jan Philip Göpfert, Barbara Hammer

The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems.

Adversarial Robustness

Prototype-based classifiers in the presence of concept drift: A modelling framework

no code implementations18 Mar 2019 Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer

We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments.

Quantization

Non-Negative Local Sparse Coding for Subspace Clustering

no code implementations12 Mar 2019 Babak Hosseini, Barbara Hammer

The NLSSC algorithm is also formulated in the kernel-based framework (NLKSSC) which can represent the nonlinear structure of data.

Clustering

Confident Kernel Sparse Coding and Dictionary Learning

no code implementations12 Mar 2019 Babak Hosseini, Barbara Hammer

In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space.

Dictionary Learning Time Series +1

Non-Negative Kernel Sparse Coding for the Classification of Motion Data

no code implementations10 Mar 2019 Babak Hosseini, Felix Hülsmann, Mario Botsch, Barbara Hammer

We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing.

Dynamic Time Warping General Classification

Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning

no code implementations8 Mar 2019 Babak Hosseini, Barbara Hammer

Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space.

Binary Classification feature selection +2

Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series

no code implementations5 Mar 2019 Babak Hosseini, Barbara Hammer

Furthermore, we obtain sparse encodings for unseen classes based on the learned MKD attributes, and upon which we propose a simple but effective incremental clustering algorithm to categorize the unseen MTS classes in an unsupervised way.

Clustering Dictionary Learning +3

FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration

no code implementations2 Mar 2019 Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann, Dominik Heider, Barbara Hammer

Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects.

feature selection General Classification +1

Adversarial attacks hidden in plain sight

1 code implementation25 Feb 2019 Jan Philip Göpfert, André Artelt, Heiko Wersing, Barbara Hammer

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue.

General Classification

Feature Relevance Bounds for Ordinal Regression

1 code implementation20 Feb 2019 Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer

The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i. e. the prediction of ordered classes.

regression

Progressive Data Science: Potential and Challenges

no code implementations19 Dec 2018 Cagatay Turkay, Nicola Pezzotti, Carsten Binnig, Hendrik Strobelt, Barbara Hammer, Daniel A. Keim, Jean-Daniel Fekete, Themis Palpanas, Yunhai Wang, Florin Rusu

We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.

Tree Edit Distance Learning via Adaptive Symbol Embeddings

no code implementations ICML 2018 Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer

Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart.

Metric Learning

The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces

no code implementations22 Aug 2017 Benjamin Paaßen, Barbara Hammer, Thomas William Price, Tiffany Barnes, Sebastian Gross, Niels Pinkwart

In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states.

Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces

1 code implementation21 Apr 2017 Benjamin Paaßen, Christina Göpfert, Barbara Hammer

We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels.

Distributed Computing Gaussian Processes +3

Feasibility Based Large Margin Nearest Neighbor Metric Learning

no code implementations18 Oct 2016 Babak Hosseini, Barbara Hammer

In this paper, we address the feasibility of LMNN's optimization constraints regarding these target points, and introduce a mathematical measure to evaluate the size of the feasible region of the optimization problem.

Metric Learning

Efficient Metric Learning for the Analysis of Motion Data

1 code implementation17 Oct 2016 Babak Hosseini, Barbara Hammer

We investigate metric learning in the context of dynamic time warping (DTW), the by far most popular dissimilarity measure used for the comparison and analysis of motion capture data.

Dynamic Time Warping General Classification +1

Optimum Reject Options for Prototype-based Classification

no code implementations23 Mar 2015 Lydia Fischer, Barbara Hammer, Heiko Wersing

We analyse optimum reject strategies for prototype-based classifiers and real-valued rejection measures, using the distance of a data point to the closest prototype or probabilistic counterparts.

Classification General Classification

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