Publications

(2024). Toward Explainable Artificial Intelligence for Precision Pathology. Annual Review of Pathology: Mechanisms of Disease.

DOI

(2024). Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks. Information Fusion.

DOI

(2024). Explaining Predictive Uncertainty by Exposing Second-Order Effects.

arXiv

(2024). Explainable AI for time series via virtual inspection layers. Pattern Recognition.

DOI

(2023). Rather a Nurse than a Physician - Contrastive Explanations under Investigation. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

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(2023). PyThaiNLP: Thai Natural Language Processing in Python. Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023).

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(2023). Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops.

(2023). Heat flux for semilocal machine-learning potentials. Physical Review B.

DOI

(2023). Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings. NeuroImage.

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(2023). Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks. The Journal of Physical Chemistry Letters.

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(2023). Set Learning for Accurate and Calibrated Models.

arXiv

(2023). Learning domain invariant representations by joint Wasserstein distance minimization. Neural Networks.

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(2023). Evaluating deep transfer learning for whole-brain cognitive decoding. Journal of the Franklin Institute.

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(2023). Concept for the Real-Time Monitoring of Molecular Configurations during Manipulation with a Scanning Probe Microscope. The Journal of Physical Chemistry C.

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(2023). Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence. Journal of Chemical Theory and Computation.

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(2023). Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation. PLOS Computational Biology.

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(2023). SchNetPack 2.0: A neural network toolbox for atomistic machine learning. The Journal of Chemical Physics.

DOI URL

(2023). Relevant Walk Search for Explaining Graph Neural Networks. Proceedings of The Fortieth International Conference on Machine Learning, ICML 2023.

(2023). Heat flux for semi-local machine-learning potentials.

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(2023). Mark My Words: Dangers of Watermarked Images in ImageNet.

arXiv

(2023). Learning trivializing gradient flows for lattice gauge theories. Physical Review D.

DOI URL

(2023). SMITH: spatially constrained stochastic model for simulation of intra-tumour heterogeneity. Bioinformatics.

DOI

(2023). Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. Transactions on Machine Learning Research.

(2023). TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. Transactions on Machine Learning Research.

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(2023). Stress and heat flux via automatic differentiation. Journal of Chemical Physics.

DOI

(2023). Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Research.

DOI URL

(2023). Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. CVPR.

(2023). Self-Supervised Training with Autoencoders for Visual Anomaly Detection.

arXiv URL

(2023). Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points. Computers & Chemical Engineering.

DOI URL

(2023). Relevant Walk Search for Explaining Graph Neural Networks. International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA.

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(2023). Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence. Journal of Chemical Theory and Computation.

(2023). Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks.

arXiv

(2023). Physics-Informed Bayesian Optimization of Variational Quantum Circuits. Advances in Neural Information Processing Systems (NeurIPS2023).

(2023). NeuLat: a toolbox for neural samplers in lattice field theories. The 40th International Symposium on Lattice Field Theory (LATTICE2023).

(2023). Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees. Transactions on Machine Learning Research.

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(2023). Local Function Complexity for Active Learning via Mixture of Gaussian Processes. Transactions on Machine Learning Research.

(2023). Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization. Neural Networks.

(2023). Labeling Neural Representations with Inverse Recognition. Advances in Neural Information Processing Systems (NeurIPS2023).

(2023). Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models. IEEE Transactions on Medical Imaging.

DOI

(2023). Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI.

arXiv

(2023). Improving neural network representations using human similarity judgments. arXiv preprint arXiv:2306.04507.

(2023). Imaging bridges pathology and radiology. Journal of Pathology Informatics.

DOI

(2023). Human alignment of neural network representations. 11th International Conference on Learning Representations, ICLR 2023, Rwanda, May 1-5, 2023.

(2023). Human alignment of neural network representations. The Eleventh International Conference on Learning Representations.

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(2023). Generative Fractional Diffusion Models. NeurIPS 2023 Workshop on Diffusion Models.

(2023). Explainable AI for Time Series via Virtual Inspection Layers.

arXiv

(2023). Explainability and Transparency in the Realm of Digital Humanities: Toward a Historian XAI. International Journal of Digital Humanities.

DOI

(2023). Drift Forensics of Malware Classifiers. Proc. of the ACM Workshop on Artificial Intelligence and Security (AISec).

(2023). Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories. Physical Review D.

(2023). Cedalion: A software framework for the analysis of multimodal fNIRS in naturalistic environments. Proc. of the first Neuroscience of the Everyday World coference.

(2023). Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI).

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(2023). Bayesian Algorithms for Joint Estimation of Brain Activity and Noise in Electromagnetic Imaging. IEEE Transactions on Medical Imaging.

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(2023). Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution. IEEE Transactions on Medical Imaging.

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(2023). Automatic identification of chemical moieties. Physical Chemistry Chemical Physics.

(2022). Introduction to the shared near infrared spectroscopy format. Neurophotonics.

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(2022). MEDICC2: Whole-Genome Doubling Aware Copy-Number Phylogenies for Cancer Evolution. Genome Biology.

DOI

(2022). Towards the integration of CW fNIRS and absolute oximetry: A proof of concept. Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). Towards a fully integrated Smart Textile patch-based cap for multi-distance CW fNIRS whole-head imaging. Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). Short-separation Regression Incorporated Diffuse Optical Tomography (SS-DOT). Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). NinjaNIRS 2022: Whole-Head, High-Density Wearable fNIRS with EEG Co-Localization. Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). Neuroscience in the everyday world: Brain correlates of naturalistic discourse in individuals with aphasia. Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). Fast and slow movement-related artifacts in fNIRS signal: what is a viable solution?. Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). Exploration of whole-head CW fNIRS-based intracranial hemorrhage detection: progress and challenges. Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). Can the fNIRS community design a standard cap layout for uniform whole-head HD fNIRS coverage? A discussion. Proc. Biennial Meeting of the Society for fNIRS 2022.

(2022). Dos and Don'ts of Machine Learning in Computer Security. Proc. of the USENIX Security Symposium.

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(2022). XAI for Transformers: Better Explanations through Conservative Propagation. Proceedings of the 39th International Conference on Machine Learning.

(2022). Equivariance versus Augmentation for Spherical Images. Proceedings of the 39th International Conference on Machine Learning.

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(2022). Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. Proceedings of the 39th International Conference on Machine Learning.

(2022). Patient-level proteomic network prediction by explainable artificial intelligence. npj Precision Oncology.

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(2022). Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data. Neurophotonics.

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(2022). High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks. Machine Learning: Science and Technology.

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(2022). VICE: Variational Interpretable Concept Embeddings. Advances in Neural Information Processing Systems.

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(2022). VICE: Variational Interpretable Concept Embeddings. Advances in Neural Information Processing Systems, 2022.

(2022). Towards robust explanations for deep neural networks. Pattern Recognition.

DOI

(2022). Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective. IEEE Signal Processing Magazine.

DOI

(2022). Sicherheit 2022, Sicherheit, Schutz und Zuverlässigkeit, Konferenzband der 11. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI), 5.-8. April 2022, Karlsruhe. Gesellschaft für Informatik e.V..

(2022). Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. arXiv.

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(2022). Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. npj Computational Materials.

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(2022). Relative energies without electronic perturbations via alchemical integral transform. The Journal of Chemical Physics.

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(2022). Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing. CODASPY ‘22: Twelveth ACM Conference on Data and Application Security and Privacy, Baltimore, MD, USA, April 24 - 27, 2022.

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(2022). PredDiff: Explanations and interactions from conditional expectations. Artificial Intelligence.

(2022). Path-Gradient Estimators for Continuous Normalizing Flows. Proceedings of the 39th International Conference on Machine Learning.

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(2022). NoiseGrad---Enhancing Explanations by Introducing Stochasticity to Model Weights. Proceedings of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022).

(2022). NinjaNIRS 2021: Continued Progress towards Whole Head, High Density fNIRS. Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN).

DOI URL

(2022). Misleading Deep-Fake Detection with GAN Fingerprints. 43rd IEEE Security and Privacy, SP Workshops 2022, San Francisco, CA, USA, May 22-26, 2022.

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(2022). Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference. NeuroImage.

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(2022). Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models. bioRxiv.

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(2022). Inverse design of 3d molecular structures with conditional generative neural networks. Nature Communications.

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(2022). Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Transactions on Pattern Analysis and Machine Intelligence.

DOI

(2022). Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL divergence for Normalizing Flows. Machine Learning: Science and Technology.

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(2022). Finding and removing Clever Hans: Using explanation methods to debug and improve deep models. Inf. Fusion.

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(2022). Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. Transactions on Machine Learning Research.

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(2022). Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study. Energies.

(2022). Empirical Bayesian localization of event-related time-frequency neural activity dynamics. NeuroImage.

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(2022). Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces. arXiv.

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(2022). Diffeomorphic Counterfactuals with Generative Models. arXiv.

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(2022). Closed Loop Feedback fNIRS Brain Computer Interface for Increasing Classification Accuracy in a Left Versus Right Hand Movement Task. Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN).

DOI URL

(2022). BIGDML---Towards accurate quantum machine learning force fields for materials. Nature Communications.

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(2022). Automatic Identification of Chemical Moieties.

arXiv

(2022). Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields. The Journal of Physical Chemistry Letters.

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(2022). A Sequential Pressure-Based Algorithm for Data-Driven Leakage Identification and Model-Based Localization in Water Distribution Networks. Journal of Water Resources Planning and Management.

(2021). Learning Interpretable Concept Groups in CNNs. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21.

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(2021). Transfer-Based Semantic Anomaly Detection. Proceedings of the 38th International Conference on Machine Learning.

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(2021). Equivariant message passing for the prediction of tensorial properties and molecular spectra. Proceedings of the 38th International Conference on Machine Learning.

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(2021). Toward Neuroscience of the Everyday World (NEW) using functional near-infrared spectroscopy. Current Opinion in Biomedical Engineering.

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(2021). Toward Constraining Mars' Thermal Evolution Using Machine Learning. American Geophysical Union (AGU).

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(2021). Real-time regression and classification of functional near infrared spectroscopy signals acquired during motor tasks. Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics.

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(2021). Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework. NeuroImage.

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(2021). THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks. Frontiers in Neuroinformatics.

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(2021). Spying through Virtual Backgrounds of Video Calls. AISec@CCS 2021: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, Virtual Event, Republic of Korea, 15 November 2021.

DOI URL

(2021). SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects. Nature Communications.

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(2021). Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy. CoRR.

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(2021). Robustifying Models Against Adversarial Attacks by Langevin Dynamics. Neural Networks.

(2021). Robust estimation of noise for electromagnetic brain imaging with the Champagne algorithm. NeuroImage.

DOI

(2021). Pruning by explaining: A novel criterion for deep neural network pruning. Pattern Recognition.

(2021). Perspective on integrating machine learning into computational chemistry and materials science. The Journal of Chemical Physics.

(2021). Optical brain imaging and its application to neurofeedback. NeuroImage: Clinical.

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(2021). On the forbidden graphene's ZO (out-of-plane optic) phononic band-analog vibrational modes in fullerenes. Communications Chemistry.

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(2021). Novel Techniques for Noise Estimation in Electromagnetic Brain Source Imaging. International Journal of Bioelectromagnetism.

(2021). Morphological and molecular breast cancer profiling through explainable machine learning. Nature Machine Intelligence.

(2021). Mixture-of-experts VAEs can disregard variation in surjective multimodal data. NeurIPS, Bayesian deep learning workshop.

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(2021). Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse. The 38th International Symposium on Lattice Field Theory (LATTICE2021).

(2021). Machine learning of solvent effects on molecular spectra and reactions. Chemical science.

(2021). Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation. The Journal of pathology.

(2021). Machine Learning Force Fields. Chemical Reviews.

DOI URL

(2021). Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization. CoRR.

(2021). Forecasting industrial aging processes with machine learning methods. Computers & Chemical Engineering.

(2021). Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications. Proc. IEEE.

(2021). Explainable Deep One-Class Classification. International Conference on Learning Representations.

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(2021). Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models. Physical review letters.

(2021). Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging. Thirty-Fifth Conference on Neural Information Processing Systems.

URL

(2021). Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature. Nature Communications.

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(2021). Diffeomorphic Explanations with Normalizing Flows. ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models.

(2021). Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation. Advances in Neural Information Processing Systems.

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(2021). Best practices for fNIRS publications. Neurophotonics.

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(2021). Automatic Identification of Types of Alterations in Historical Manuscripts. Digital Humanities Quarterly.

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(2021). A Unifying Review of Deep and Shallow Anomaly Detection. Proceedings of the IEEE.

DOI

(2021). 3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds. J. Phys. Chem. B.

(2021). High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics.

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(2020). Open Access Multimodal fNIRS Resting State Dataset With and Without Synthetic Hemodynamic Responses. Frontiers in Neuroscience.

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(2020). A machine-learning-based surrogate model of Mars' thermal evolution. Oxford University Press (OUP).

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(2020). Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Frontiers in Human Neuroscience.

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(2020). Towards Neuroscience in the Everyday World: Progress in wearable fNIRS instrumentation and applications. Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN).

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(2020). Towards explaining anomalies: A deep Taylor decomposition of one-class models. Pattern Recognition.

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(2020). Towards Best Practice in Explaining Neural Network Decisions with LRP. Proceedings of International Joint Conference on Neural Networks (IJCNN2020).

(2020). The Clever Hans Effect in Anomaly Detection. CoRR.

(2020). The autofeat Python Library for Automatic Feature Engineering and Selection. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019.

DOI

(2020). Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Scientific reports.

(2020). Quantum chemical accuracy from density functional approximations via machine learning. Nature communications.

(2020). Optimizing for Measure of Performance in Max-Margin Parsing. IEEE Transactions on Neural Networks and Learning Systems.

(2020). Noise Learning in Empirical Bayesian Source Reconstruction Algorithms for Electromagnetic Brain Imaging. The Organization for Human Brain Mapping (OHBM).

(2020). Motor Imagery under Distraction - An open access BCI dataset. frontneurosci.

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(2020). Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields. The Journal of Chemical Physics.

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(2020). Machine-learning Inference of the Interior Structure of Low-mass Exoplanets. American Astronomical Society.

DOI URL

(2020). Joint Hierarchical Bayesian Learning of Full-structure Noise for Brain Source Imaging. Thirty-Forth Conference on Neural Information Processing Systems (NeurIPS), Medical Imaging meets NeurIPS (Med-NeurIPS) Workshop.

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(2020). Introduction to Neural Networks. Springer International Publishing.

DOI URL

(2020). Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images. Artificial Intelligence and Machine Learning for Digital Pathology.

(2020). Input Hessian Regularization of Neural Networks. International Conference on Machine Learning: Workshop on Beyond First Order Methods in Machine Learning.

(2020). Improving nonparametric density estimation with tensor decompositions. arXiv preprint arXiv:2010.02425.

(2020). GraphKKE: graph Kernel Koopman embedding for human microbiome analysis. Appl. Netw. Sci..

(2020). Fairwashing explanations with off-manifold detergent. Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event.

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(2020). Electromagnetic Brain Imaging using Sparse Bayesian Learning – Noise Learning and Model Selection. The Organization for Human Brain Mapping (OHBM).

(2020). Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations. Advances in Neural Information Processing Systems.

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(2020). Building and Interpreting Deep Similarity Models. IEEE Transactions on Pattern Analysis and Machine Intelligence.

DOI

(2020). Benign Examples: Imperceptible changes can enhance image translation performance. Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI2020).

(2020). Asymptotically Unbiased Estimation of Physical Observables with Neural Samplers. Physical Review E.

(2020). Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks. Proceedings of IEEE International Conference on Communications (ICC2020).

(2020). A Proposal for Supervised Density Estimation. NeurIPS Pre-Registration Workshop.

(2020). A deep neural network for molecular wave functions in quasi-atomic minimal basis representation. The Journal of Chemical Physics.

(2019). A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy. NeuroImage.

DOI URL

(2019). Variational Bayesian Learning Theory. Cambridge University Press.

(2019). Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. Nature Communications.

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(2019). Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions. CoRR.

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(2019). Understanding Patch-Based Learning of Video Data by Explaining Predictions. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.

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(2019). Towards Explainable Artificial Intelligence. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.

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(2019). Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. Advances in Neural Information Processing Systems 32.

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(2019). Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication. International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14-19, 2019.

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(2019). sGDML: Constructing accurate and data efficient molecular force fields using machine learning. Computer Physics Communications.

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(2019). Self-attentive, multi-context one-class classification for unsupervised anomaly detection on text. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

(2019). SchNetPack: A Deep Learning Toolbox For Atomistic Systems. Journal of chemical theory and computation.

(2019). Rotation Invariant Clustering of 3D Cell Nuclei Shapes*. 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

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(2019). Robustifying Models Against Adversarial Attacks by Langevin Dynamics. ICML Workshop on Uncertainty & Robustness in Deep Learning.

(2019). Robust and Communication-Efficient Federated Learning from Non-IID Data. CoRR.

arXiv URL

(2019). Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. CoRR.

(2019). Quantum-Chemical Insights from Interpretable Atomistic Neural Networks. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.

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(2019). Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS2019).

(2019). N-ary decomposition for multi-class classification. Machine Learning.

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(2019). Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces. The Journal of Chemical Physics.

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(2019). Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Science translational medicine.

(2019). Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Science Translational Medicine.

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(2019). Layer-Wise Relevance Propagation: An Overview. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.

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(2019). iNNvestigate neural networks!. Journal of Machine Learning Research.

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(2019). Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.

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(2019). From Clustering to Cluster Explanations via Neural Networks. CoRR.

arXiv URL

(2019). Explanations can be manipulated and geometry is to blame. Advances in Neural Information Processing Systems 32.

(2019). Explaining the unique nature of individual gait patterns with deep learning. Scientific Reports.

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(2019). Explaining and Interpreting LSTMs. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.

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(2019). Explainable Deep Learning for Analysing Brain Data. 2019 7th International Winter Conference on Brain-Computer Interface (BCI).

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(2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer.

(2019). Evaluating Recurrent Neural Network Explanations. Proceedings of the ACL'19 Workshop on BlackboxNLP.

(2019). Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network. Digital Signal Processing.

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(2019). Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation. NeuroImage.

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(2019). Defense Against Adversarial Attacks by Langevin Dynamics.

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(2019). Deep Semi-Supervised Anomaly Detection. CoRR.

arXiv URL

(2019). Deep Learning for Proteomics Data for Feature Selection and Classification. Machine Learning and Knowledge Extraction.

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(2019). Deep Brain Source Imaging: An LSTM-inspired Approach for EEG Source Localization based on Sparse Bayesian Learning. Signal Processing with Adaptive Sparse Structured Representations (SPARS).

(2019). Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights. CoRR.

arXiv

(2019). Compact and Computationally Efficient Representation of Deep Neural Networks. IEEE Transactions on Neural Networks and Learning Systems.

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(2019). Comment on\" Solving Statistical Mechanics Using VANs\": Introducing saVANt-VANs Enhanced by Importance and MCMC Sampling. arXiv preprint arXiv:1903.11048.

(2019). Classification of structured validation data using stateless and stateful features. Computer Communications.

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(2019). Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets. NeuroImage.

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(2019). Black-Box Decision based Adversarial Attack with Symmetric Alpha-stable Distribution. Proceedings of the European Signal Processing Conference (EUSIPCO2019).

(2019). Black-Box Decision based Adversarial Attack with Symmetric $α$-stable Distribution. CoRR.

arXiv URL

(2019). Automating the search for a patent's prior art with a full text similarity search. PLoS ONE.

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(2019). Automated Documentation of End-to-End Experiments in Data Science. 2019 IEEE 35th International Conference on Data Engineering (ICDE).

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(2018). Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers. Genome Medicine.

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(2018). Wasserstein Stationary Subspace Analysis. IEEE Journal of Selected Topics in Signal Processing.

(2018). Unsupervised Detection and Explanation of Latent-class Contextual Anomalies. CoRR.

arXiv URL

(2018). Transductive Regression for Data with Latent Dependency Structure. IEEE Transactions on Neural Networks and Learning Systems.

(2018). Towards exact molecular dynamics simulations with machine-learned force fields. Nature Communications.

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(2018). Support Vector Data Descriptions and K-means Clustering: One Class?. IEEE Transactions on Neural Networks and Learning Systems.

(2018). Structuring Neural Networks for More Explainable Predictions. Explainable and Interpretable Models in Computer Vision and Machine Learning.

DOI URL

(2018). Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset. Scientific Data.

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(2018). Sharing Hash Codes for Multiple Purposes. Japanese Journal of Statistics and Data Science.

(2018). Sharing hash codes for multiple purposes. Japanese Journal of Statistics and Data Science.

(2018). Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning. Seminars in cancer biology.

(2018). SchNet--A deep learning architecture for molecules and materials. The Journal of Chemical Physics.

(2018). Predicting Pairwise Relations with Neural Similarity Encoders. Bulletin of the Polish Academy of Sciences: Technical Sciences.

DOI

(2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing.

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(2018). Learning how to explain neural networks: PatternNet and PatternAttribution. 6th International Conference on Learning Representations.

(2018). Improving EEG Source Localization Through Spatio-Temporal Sparse Bayesian Learning. 2018 26th European Signal Processing Conference (EUSIPCO).

DOI URL

(2018). Generating equilibrium molecules with deep neural networks. CoRR.

arXiv URL

(2018). Entropy-Constrained Training of Deep Neural Networks.

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(2018). Deep One-Class Classification. International Conference on Machine Learning (ICML).

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(2018). Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment. IEEE Trans. Image Processing.

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(2018). Backprop Evolution. ICML 2018 AutoML Workshop.

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(2018). Analysis of Atomistic Representations Using Weighted Skip-Connections.

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(2017). Headgear for mobile neurotechnology: looking into alternatives for EEG and NIRS probes. Proceedings of the 7th Graz Brain-Computer Interface Conference 2017.

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(2017). Headgear for mobile neurotechnology: looking into alternatives for EEG and NIRS probes. Proceedings of the 7th Graz Brain-Computer Interface Conference 2017.

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(2017). Context encoders as a simple but powerful extension of word2vec. Proceedings of the 2nd Workshop on Representation Learning for NLP.

(2017). \"What is relevant in a text document?\": An interpretable machine learning approach. PLOS ONE.

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(2017). Why build an integrated EEG-NIRS? About the advantages of hybrid bio-acquisition hardware. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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(2017). Why build an integrated EEG-NIRS? About the advantages of hybrid bio-acquisition hardware. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

DOI

(2017). M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring. IEEE Transactions on Biomedical Engineering.

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(2017). M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring. IEEE Transactions on Biomedical Engineering.

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(2017). Distributed optimization of multi-class SVMs. PLOS ONE.

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(2017). Variational Robust Subspace Clustering with Mean Update Algorithm. ICCV Workshop on Robust Subspace Learning and Applications in Computer Vision (RSL-CV).

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