xxAI - Beyond Explainable AI
International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
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121 Result(s)
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Klaus-Robert Müller
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Chapter and Conference Paper
Mark My Words: Dangers of Watermarked Images in ImageNet
The utilization of pre-trained networks, especially those trained on ImageNet, has become a common practice in Computer Vision. However, prior research has indicated that a significant number of images in the ...
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Article
Open AccessExplainability and transparency in the realm of digital humanities: toward a historian XAI
The recent advancements in the field of Artificial Intelligence (AI) translated to an increased adoption of AI technology in the humanities, which is often challenged by the limited amount of annotated data, a...
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Article
Open AccessDNA methylation-based classification of sinonasal tumors
The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SN...
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Article
Open AccessNew definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning
Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluo...
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Article
Open AccessAn Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning
The Sphere project stands at the intersection of the humanities and information sciences. The project aims to better understand the evolution of knowledge in the early modern period by studying a collection of...
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Article
Open AccessBIGDML—Towards accurate quantum machine learning force fields for materials
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that rest...
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Article
Open AccessPatient-level proteomic network prediction by explainable artificial intelligence
Understanding the pathological properties of dysregulated protein networks in individual patients’ tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of t...
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Article
Künstliche Intelligenz als Lösung des PathologInnenmangels?
Angesichts der rasanten Entwicklungen wird kaum bezweifelt, dass die künstliche Intelligenz (KI) die pathologische Diagnostik nachhaltig beeinflussen wird. Ob allerdings KI in erster Linie ein weiteres diagnos...
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Article
Open AccessScrutinizing XAI using linear ground-truth data with suppressor variables
Machine learning (ML) is increasingly often used to inform high-stakes decisions. As complex ML models (e.g., deep neural networks) are often considered black boxes, a wealth of procedures has been developed t...
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Article
Open AccessInverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distr...
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Chapter
xxAI - Beyond Explainable Artificial Intelligence
The success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the resul...
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Book
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Chapter
Explaining the Predictions of Unsupervised Learning Models
Unsupervised learning is a subfield of machine learning that focuses on learning the structure of data without making use of labels. This implies a different set of learning algorithms than those used for supe...
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Article
Open AccessSpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Machine-learned force fields combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current machine-learned force fields typically ignore electronic degrees of fre...
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Article
Morphological and molecular breast cancer profiling through explainable machine learning
Recent advances in cancer research and diagnostics largely rely on new developments in microscopic or molecular profiling techniques, offering high levels of detail with respect to either spatial or molecular ...
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Article
Open AccessDynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the inclusion of the zero point energy and its coupling with the anharmonicities in interatomic interactions. Here, we prese...
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Article
Open AccessQuantum chemical accuracy from density functional approximations via machine learning
Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol−1 with presently-available functionals. Ab initio method...
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Article
Open AccessRisk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work...
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Article
Exploring chemical compound space with quantum-based machine learning
Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. R...
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Article
Open AccessResolving challenges in deep learning-based analyses of histopathological images using explanation methods
Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard qua...
