Author name cluster

Alexander Binder

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8 papers

2 author rows

TMLR Journal 2025 Journal Article

Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation

Leander Weber
Jim Berend
Moritz Weckbecker
Alexander Binder
Thomas Wiegand
Wojciech Samek
Sebastian Lapuschkin

Gradient-based optimization has been a cornerstone of machine learning that enabled the vast ad- vances of Artificial Intelligence (AI) development over the past decades. However, this type of optimization requires differentiation, and with recent evidence of the benefits of non-differentiable (e.g. neuromorphic) architectures over classical models w.r.t. efficiency, such constraints can be- come limiting in the future. We present Layer-wise Feedback Propagation (LFP), a novel training principle for neural network-like predictors that utilizes methods from the domain of explainability to decompose a reward to individual neurons based on their respective contributions. Leveraging these neuron-wise rewards, our method then implements a greedy approach reinforcing helpful parts of the network and weakening harmful ones. While having comparable computational complexity to gradient descent, LFP does not require gradient computation and generates sparse and thereby memory- and energy-efficient parameter updates and models. We establish the convergence of LFP theoretically and empirically, demonstrating its effectiveness on various models and datasets. Via two applications — neural network pruning and the approximation-free training of Spiking Neural Networks (SNNs) — we demonstrate that LFP combines increased efficiency in terms of computation and representation with flexibility w.r.t. choice of model architecture and objective function.

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NeurIPS Conference 2024 Conference Paper

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology

Dhananjay Tomar
Alexander Binder
Andreas Kleppe

Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment. We hypothesise that focusing on nuclei can improve the out-of-domain (OOD) generalisation in cancer detection. We propose a simple approach to improve OOD generalisation for cancer detection by focusing on nuclear morphology and organisation, as these are domain-invariant features critical in cancer detection. Our approach integrates original images with nuclear segmentation masks during training, encouraging the model to prioritise nuclei and their spatial arrangement. Going beyond mere data augmentation, we introduce a regularisation technique that aligns the representations of masks and original images. We show, using multiple datasets, that our method improves OOD generalisation and also leads to increased robustness to image corruptions and adversarial attacks. The source code is available at https: //github. com/undercutspiky/SFL/

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ICLR Conference 2020 Conference Paper

Deep Semi-Supervised Anomaly Detection

Lukas Ruff
Robert A. Vandermeulen
Nico Görnitz
Alexander Binder
Emmanuel Müller
Klaus-Robert Müller
Marius Kloft

Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous. Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples. Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific. In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection. We further introduce an information-theoretic framework for deep anomaly detection based on the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution, which can serve as a theoretical interpretation for our method. In extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, we demonstrate that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.

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ICML Conference 2018 Conference Paper

Deep One-Class Classification

Lukas Ruff
Nico Görnitz
Lucas Deecke
Shoaib Ahmed Siddiqui
Robert A. Vandermeulen
Alexander Binder
Emmanuel Müller
Marius Kloft

Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection. Those approaches which do exist involve networks trained to perform a task other than anomaly detection, namely generative models or compression, which are in turn adapted for use in anomaly detection; they are not trained on an anomaly detection based objective. In this paper we introduce a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective. The adaptation to the deep regime necessitates that our neural network and training procedure satisfy certain properties, which we demonstrate theoretically. We show the effectiveness of our method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.

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JMLR Journal 2016 Journal Article

The LRP Toolbox for Artificial Neural Networks

Sebastian Lapuschkin
Alexander Binder
Grégoire Montavon
Klaus-Robert Müller
Wojciech Samek

The Layer-wise Relevance Propagation (LRP) algorithm explains a classifier's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself. With the LRP Toolbox we provide platform-agnostic implementations for explaining the predictions of pre-trained state of the art Caffe networks and stand-alone implementations for fully connected Neural Network models. The implementations for Matlab and python shall serve as a playing field to familiarize oneself with the LRP algorithm and are implemented with readability and transparency in mind. Models and data can be imported and exported using raw text formats, Matlab's.mat files and the.npy format for numpy or plain text. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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YNIMG Journal 2015 Journal Article

Extracting latent brain states — Towards true labels in cognitive neuroscience experiments

Anne K. Porbadnigk
Nico Görnitz
Claudia Sannelli
Alexander Binder
Mikio Braun
Marius Kloft
Klaus-Robert Müller

Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the threshold of perception is measured, the error distribution deviates from uniformity due to the structure in the underlying experimental set-up. When we base our analysis on the behavioral labels as usually done, then we ignore this problem of systematic and structured (non-uniform) label noise and are likely to arrive at wrong conclusions in our data analysis. This paper contributes a remedy to this important scenario: we present a novel approach for a) measuring label noise and b) removing structured label noise. We demonstrate its usefulness for EEG data analysis using a standard d2 test for visual attention (N=20 participants).

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NeurIPS Conference 2015 Conference Paper

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms

Yunwen Lei
Urun Dogan
Alexander Binder
Marius Kloft

This paper studies the generalization performance of multi-class classification algorithms, for which we obtain, for the first time, a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent generalization analysis. The theoretical analysis motivates us to introduce a new multi-class classification machine based on lp-norm regularization, where the parameter p controls the complexity of the corresponding bounds. We derive an efficient optimization algorithm based on Fenchel duality theory. Benchmarks on several real-world datasets show that the proposed algorithm can achieve significant accuracy gains over the state of the art.

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JMLR Journal 2010 Journal Article

The SHOGUN Machine Learning Toolbox

Sören Sonnenburg
Gunnar Rätsch
Sebastian Henschel
Christian Widmer
Jonas Behr
Alexander Zien
Fabio de Bona
Alexander Binder

We have developed a machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers a considerable number of machine learning models such as support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis, and more. Most of the specific algorithms are able to deal with several different data classes. We have used this toolbox in several applications from computational biology, some of them coming with no less than 50 million training examples and others with 7 billion test examples. With more than a thousand installations worldwide, SHOGUN is already widely adopted in the machine learning community and beyond. SHOGUN is implemented in C++ and interfaces to MATLAB TM, R, Octave, Python, and has a stand-alone command line interface. The source code is freely available under the GNU General Public License, Version 3 at http://www.shogun-toolbox.org. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2010. ( edit, beta )

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