% % This file was created by the TYPO3 extension % bib % --- Timezone: CEST % Creation date: 2024-04-16 % Creation time: 21-57-38 % --- Number of references % 22 % @Article { 10.3389/frai.2024.1330919, title = {Benchmarking the influence of pre-training on explanation performance in MR image classification}, journal = {Frontiers in Artificial Intelligence}, year = {2024}, volume = {7}, abstract = {Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of “explainable” artificial intelligence (XAI). However, previous studies have rarely quantitatively evaluated the “explanation performance” of XAI methods against ground-truth data, and transfer learning and its influence on objective measures of explanation performance has not been investigated. Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task. We employ this benchmark to understand the influence of transfer learning on the quality of explanations. Experimental results show that popular XAI methods applied to the same underlying model differ vastly in performance, even when considering only correctly classified examples. We further observe that explanation performance strongly depends on the task used for pre-training and the number of CNN layers pre-trained. These results hold after correcting for a substantial correlation between explanation and classification performance.}, tags = {8.4,8.44}, web_url = {https://www.frontiersin.org/articles/10.3389/frai.2024.1330919}, ISSN = {2624-8212}, DOI = {10.3389/frai.2024.1330919}, author = {Oliveira, Marta and Wilming, Rick and Clark, Benedict and Budding, C{\'e}line and Eitel, Fabian and Ritter, Kerstin and Haufe, Stefan} } @Inproceedings { pmlr-v202-wilming23a, title = {Theoretical Behavior of {XAI} Methods in the Presence of Suppressor Variables}, year = {2023}, month = {7}, volume = {202}, pages = {37091--37107}, abstract = {In recent years, the community of ’explainable artificial intelligence’ (XAI) has created a vast body of methods to bridge a perceived gap between model ’complexity’ and ’interpretability’. However, a concrete problem to be solved by XAI methods has not yet been formally stated. As a result, XAI methods are lacking theoretical and empirical evidence for the ’correctness’ of their explanations, limiting their potential use for quality-control and transparency purposes. At the same time, Haufe et al. (2014) showed, using simple toy examples, that even standard interpretations of linear models can be highly misleading. Specifically, high importance may be attributed to so-called suppressor variables lacking any statistical relation to the prediction target. This behavior has been confirmed empirically for a large array of XAI methods in Wilming et al. (2022). Here, we go one step further by deriving analytical expressions for the behavior of a variety of popular XAI methods on a simple two-dimensional binary classification problem involving Gaussian class-conditional distributions. We show that the majority of the studied approaches will attribute non-zero importance to a non-class-related suppressor feature in the presence of correlated noise. This poses important limitations on the interpretations and conclusions that the outputs of these XAI methods can afford.}, tags = {8.4,8.44}, web_url = {https://proceedings.mlr.press/v202/wilming23a.html}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, author = {Wilming, Rick and Kieslich, Leo and Clark, Benedict and Haufe, Stefan} } @Article { cesnaite_alterations_2023, title = {Alterations in rhythmic and non‐rhythmic resting‐state {EEG} activity and their link to cognition in older age}, journal = {NeuroImage}, year = {2023}, volume = {268}, pages = {119810}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811922009314}, web_url_date = {2023-08-22}, language = {en}, ISSN = {10538119}, DOI = {10.1016/j.neuroimage.2022.119810}, author = {Cesnaite, Elena and Steinfath, Paul and Jamshidi Idaji, Mina and Stephani, Tilman and Kumral, Deniz and Haufe, Stefan and Sander, Christian and Hensch, Tilman and Hegerl, Ulrich and Riedel-Heller, Steffi and R{\"o}hr, Susanne and Schroeter, Matthias L. and Witte, A．Veronica and Villringer, Arno and Nikulin, Vadim V.} } @Article { pellegrini_identifying_2023, title = {Identifying good practices for detecting inter-regional linear functional connectivity from {EEG}}, journal = {NeuroImage}, year = {2023}, volume = {277}, pages = {120218}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811923003695}, web_url_date = {2023-08-22}, language = {en}, ISSN = {10538119}, DOI = {10.1016/j.neuroimage.2023.120218}, author = {Pellegrini, Franziska and Delorme, Arnaud and Nikulin, Vadim and Haufe, Stefan} } @Article { cai_bayesian_2023, title = {Bayesian {Algorithms} for {Joint} {Estimation} of {Brain} {Activity} and {Noise} in {Electromagnetic} {Imaging}}, journal = {IEEE Transactions on Medical Imaging}, year = {2023}, volume = {42}, number = {3}, pages = {762--773}, tags = {8.4,8.44}, web_url = {https://ieeexplore.ieee.org/document/9932439/}, web_url_date = {2023-08-22}, ISSN = {0278-0062, 1558-254X}, DOI = {10.1109/TMI.2022.3218074}, author = {Cai, Chang and Kang, Huicong and Hashemi, Ali and Chen, Dan and Diwakar, Mithun and Haufe, Stefan and Sekihara, Kensuke and Wu, Wei and Nagarajan, Srikantan S.} } @Article { haufe_gait_2023, title = {Gait {Event} {Prediction} {Using} {Surface} {Electromyography} in {Parkinsonian} {Patients}}, journal = {Bioengineering}, year = {2023}, volume = {10}, number = {2}, pages = {212}, abstract = {Gait disturbances are common manifestations of Parkinson’s disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of {\textbackslashtextless}50 ms), low numbers of missed events ({\textbackslashtextless}2\%), and next to no false-positive event detections ({\textbackslashtextless}0.1\%). Swing and stance phases could thus be determined with high fidelity (median F1-score of {\textbackslashtextasciitilde}0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.}, tags = {8.4, 8.44}, web_url = {https://www.mdpi.com/2306-5354/10/2/212}, web_url_date = {2023-08-22}, language = {en}, ISSN = {2306-5354}, DOI = {10.3390/bioengineering10020212}, author = {Haufe, Stefan and Isaias, Ioannis U. and Pellegrini, Franziska and Palmisano, Chiara} } @Article { cai_bayesian_2023-1, title = {Bayesian adaptive beamformer for robust electromagnetic brain imaging of correlated sources in high spatial resolution}, journal = {IEEE Transactions on Medical Imaging}, year = {2023}, pages = {1--1}, tags = {8.4,8.44}, web_url = {https://ieeexplore.ieee.org/document/10068553/}, web_url_date = {2023-08-22}, ISSN = {0278-0062, 1558-254X}, DOI = {10.1109/TMI.2023.3256963}, author = {Cai, Chang and Long, Yuanshun and Ghosh, Sanjay and Hashemi, Ali and Gao, Yijing and Diwakar, Mithun and Haufe, Stefan and Sekihara, Kensuke and Wu, Wei and Nagarajan, Srikantan S.} } @Article { del_vecchio_del_vecchio_pallidal_2023, title = {Pallidal {Recordings} in {Chronically} {Implanted} {Dystonic} {Patients}: {Mitigation} of {Tremor}-{Related} {Artifacts}}, journal = {Bioengineering}, year = {2023}, volume = {10}, number = {4}, pages = {476}, abstract = {Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the PerceptTM PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.}, tags = {8.4, 8.44}, web_url = {https://www.mdpi.com/2306-5354/10/4/476}, web_url_date = {2023-08-22}, language = {en}, ISSN = {2306-5354}, DOI = {10.3390/bioengineering10040476}, author = {Del Vecchio Del Vecchio, Jasmin and Hanafi, Ibrahem and Pozzi, Nicol{\'o} Gabriele and Capetian, Philipp and Isaias, Ioannis U. and Haufe, Stefan and Palmisano, Chiara} } @Article { cai_empirical_2022, title = {Empirical {Bayesian} localization of event-related time-frequency neural activity dynamics}, journal = {NeuroImage}, year = {2022}, volume = {258}, pages = {119369}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811922004888}, web_url_date = {2022-09-13}, language = {en}, ISSN = {10538119}, DOI = {10.1016/j.neuroimage.2022.119369}, author = {Cai, Chang and Hinkley, Leighton and Gao, Yijing and Hashemi, Ali and Haufe, Stefan and Sekihara, Kensuke and Nagarajan, Srikantan S.} } @Article { langer_benchmark_2022, title = {A benchmark for prediction of psychiatric multimorbidity from resting {EEG} data in a large pediatric sample}, journal = {NeuroImage}, year = {2022}, volume = {258}, pages = {119348}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811922004670}, web_url_date = {2022-09-13}, language = {en}, ISSN = {10538119}, DOI = {10.1016/j.neuroimage.2022.119348}, author = {Langer, Nicolas and Plomecka, Martyna Beata and Tr{\"o}ndle, Marius and Negi, Anuja and Popov, Tzvetan and Milham, Michael and Haufe, Stefan} } @Article { wilming_scrutinizing_2022, title = {Scrutinizing {XAI} using linear ground-truth data with suppressor variables}, journal = {Machine Learning}, year = {2022}, volume = {111}, number = {5}, pages = {1903--1923}, abstract = {Abstract 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 to shed light on their inner workings and the ways in which their predictions come about, defining the field of ‘explainable AI’ (XAI). Saliency methods rank input features according to some measure of ‘importance’. Such methods are difficult to validate since a formal definition of feature importance is, thus far, lacking. It has been demonstrated that some saliency methods can highlight features that have no statistical association with the prediction target (suppressor variables). To avoid misinterpretations due to such behavior, we propose the actual presence of such an association as a necessary condition and objective preliminary definition for feature importance. We carefully crafted a ground-truth dataset in which all statistical dependencies are well-defined and linear, serving as a benchmark to study the problem of suppressor variables. We evaluate common explanation methods including LRP, DTD, PatternNet, PatternAttribution, LIME, Anchors, SHAP, and permutation-based methods with respect to our objective definition. We show that most of these methods are unable to distinguish important features from suppressors in this setting.}, tags = {8.4, 8.44}, web_url = {https://link.springer.com/10.1007/s10994-022-06167-y}, web_url_date = {2022-09-13}, language = {en}, ISSN = {0885-6125, 1573-0565}, DOI = {10.1007/s10994-022-06167-y}, author = {Wilming, Rick and Budding, C{\'e}line and M{\"u}ller, Klaus-Robert and Haufe, Stefan} } @Article { kumral_relationship_2022, title = {Relationship between regional white matter hyperintensities and alpha oscillations in older adults}, journal = {Neurobiology of Aging}, year = {2022}, volume = {112}, pages = {1--11}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S0197458021003195}, web_url_date = {2022-09-13}, language = {en}, ISSN = {01974580}, DOI = {10.1016/j.neurobiolaging.2021.10.006}, author = {Kumral, Deniz and Cesnaite, Elena and Beyer, Frauke and Hofmann, Simon M. and Hensch, Tilman and Sander, Christian and Hegerl, Ulrich and Haufe, Stefan and Villringer, Arno and Witte, A. Veronica and Nikulin, Vadim V.} } @Article { merk_machine_2022, title = {Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation}, journal = {Experimental Neurology}, year = {2022}, volume = {351}, pages = {113993}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S0014488622000188}, web_url_date = {2022-09-13}, language = {en}, ISSN = {00144886}, DOI = {10.1016/j.expneurol.2022.113993}, author = {Merk, Timon and Peterson, Victoria and K{\"o}hler, Richard and Haufe, Stefan and Richardson, R. Mark and Neumann, Wolf-Julian} } @Article { hashemi_joint_2022, title = {Joint {Learning} of {Full}-structure {Noise} in {Hierarchical} {Bayesian} {Regression} {Models}}, journal = {IEEE Transactions on Medical Imaging}, year = {2022}, pages = {1--1}, tags = {8.4, 8.44}, web_url = {https://ieeexplore.ieee.org/document/9963991/}, web_url_date = {2023-08-22}, ISSN = {0278-0062, 1558-254X}, DOI = {10.1109/TMI.2022.3224085}, author = {Hashemi, Ali and Cai, Chang and Gao, Yijing and Ghosh, Sanjay and M{\"u}ller, Klaus-Robert and Nagarajan, Srikantan S. and Haufe, Stefan} } @Article { pellegrini_p_2022, title = {P 80 {How} to avoid measurement of spurious inter-regional functional connectivity from {EEG} – a simulation study}, journal = {Clinical Neurophysiology}, year = {2022}, volume = {137}, pages = {e60--e61}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S1388245722001298}, web_url_date = {2023-08-22}, language = {en}, ISSN = {13882457}, DOI = {10.1016/j.clinph.2022.01.111}, author = {Pellegrini, F. and Nikulin, V. and Haufe, S.} } @Article { noauthor_erratum:_2022, title = {Erratum: {Stephani} et al., “{Temporal} {Signatures} of {Criticality} in {Human} {Cortical} {Excitability} as {Probed} by {Early} {Somatosensory} {Responses}”}, journal = {The Journal of Neuroscience}, year = {2022}, volume = {42}, number = {22}, pages = {4605--4605}, tags = {8.4, 8.44}, web_url = {https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.0820-22.2022}, web_url_date = {2023-08-22}, language = {en}, ISSN = {0270-6474, 1529-2401}, DOI = {10.1523/JNEUROSCI.0820-22.2022} } @Article { palmisano_gait_2022, title = {Gait {Initiation} {Impairment} in {Patients} with {Parkinson}’s {Disease} and {Freezing} of {Gait}}, journal = {Bioengineering}, year = {2022}, volume = {9}, number = {11}, pages = {639}, abstract = {Freezing of gait (FOG) is a sudden episodic inability to produce effective stepping despite the intention to walk. It typically occurs during gait initiation (GI) or modulation and may lead to falls. We studied the anticipatory postural adjustments (imbalance, unloading, and stepping phase) at GI in 23 patients with Parkinson’s disease (PD) and FOG (PDF), 20 patients with PD and no previous history of FOG (PDNF), and 23 healthy controls (HCs). Patients performed the task when off dopaminergic medications. The center of pressure (CoP) displacement and velocity during imbalance showed significant impairment in both PDNF and PDF, more prominent in the latter patients. Several measurements were specifically impaired in PDF patients, especially the CoP displacement along the anteroposterior axis during unloading. The pattern of segmental center of mass (SCoM) movements did not show differences between groups. The standing postural profile preceding GI did not correlate with outcome measurements. We have shown impaired motor programming at GI in Parkinsonian patients. The more prominent deterioration of unloading in PDF patients might suggest impaired processing and integration of somatosensory information subserving GI. The unaltered temporal movement sequencing of SCoM might indicate some compensatory cerebellar mechanisms triggering time-locked models of body mechanics in PD.}, tags = {8.4, 8.44}, web_url = {https://www.mdpi.com/2306-5354/9/11/639}, web_url_date = {2023-08-22}, language = {en}, ISSN = {2306-5354}, DOI = {10.3390/bioengineering9110639}, author = {Palmisano, Chiara and Beccaria, Laura and Haufe, Stefan and Volkmann, Jens and Pezzoli, Gianni and Isaias, Ioannis U.} } @Article { oala_machine_2021, title = {Machine {Learning} for {Health}: {Algorithm} {Auditing} \& {Quality} {Control}}, journal = {Journal of Medical Systems}, year = {2021}, volume = {45}, number = {12}, pages = {105}, abstract = {Abstract Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing \& Quality Control in this journal to advance the practice of ML4H auditing.}, tags = {8.4, 8.44}, web_url = {https://link.springer.com/10.1007/s10916-021-01783-y}, web_url_date = {2022-09-13}, language = {en}, ISSN = {0148-5598, 1573-689X}, DOI = {10.1007/s10916-021-01783-y}, author = {Oala, Luis and Murchison, Andrew G. and Balachandran, Pradeep and Choudhary, Shruti and Fehr, Jana and Leite, Alixandro Werneck and Goldschmidt, Peter G. and Johner, Christian and Sch{\"o}rverth, Elora D. M. and Nakasi, Rose and Meyer, Martin and Cabitza, Federico and Baird, Pat and Prabhu, Carolin and Weicken, Eva and Liu, Xiaoxuan and Wenzel, Markus and Vogler, Steffen and Akogo, Darlington and Alsalamah, Shada and Kazim, Emre and Koshiyama, Adriano and Piechottka, Sven and Macpherson, Sheena and Shadforth, Ian and Geierhofer, Regina and Matek, Christian and Krois, Joachim and Sanguinetti, Bruno and Arentz, Matthew and Bielik, Pavol and Calderon-Ramirez, Saul and Abbood, Auss and Langer, Nicolas and Haufe, Stefan and Kherif, Ferath and Pujari, Sameer and Samek, Wojciech and Wiegand, Thomas} } @Article { hashemi_unification_2021, title = {Unification of sparse {Bayesian} learning algorithms for electromagnetic brain imaging with the majorization minimization framework}, journal = {NeuroImage}, year = {2021}, volume = {239}, pages = {118309}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921005851}, web_url_date = {2022-09-13}, language = {en}, ISSN = {10538119}, DOI = {10.1016/j.neuroimage.2021.118309}, author = {Hashemi, Ali and Cai, Chang and Kutyniok, Gitta and M{\"u}ller, Klaus-Robert and Nagarajan, Srikantan S. and Haufe, Stefan} } @Article { cai_robust_2021, title = {Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm}, journal = {NeuroImage}, year = {2021}, volume = {225}, pages = {117411}, tags = {8.4, 8.44}, web_url = {https://linkinghub.elsevier.com/retrieve/pii/S105381192030896X}, web_url_date = {2022-09-13}, language = {en}, ISSN = {10538119}, DOI = {10.1016/j.neuroimage.2020.117411}, author = {Cai, Chang and Hashemi, Ali and Diwakar, Mithun and Haufe, Stefan and Sekihara, Kensuke and Nagarajan, Srikantan S.} } @Article { lichtner_predicting_2021, title = {Predicting lethal courses in critically ill {COVID}-19 patients using a machine learning model trained on patients with non-{COVID}-19 viral pneumonia}, journal = {Scientific Reports}, year = {2021}, volume = {11}, number = {1}, pages = {13205}, abstract = {Abstract In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95\% CI 0.86–0.87]) than the clinical scores APACHE2 (0.63 [95\% CI 0.61–0.65]), SAPS2 (0.72 [95\% CI 0.71–0.74]) and SOFA (0.76 [95\% CI 0.75–0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95\% CI 0.73–0.78]) and Wang (laboratory: 0.62 [95\% CI 0.59–0.65]; clinical: 0.56 [95\% CI 0.55–0.58]) and the 4C COVID-19 Mortality score (0.71 [95\% CI 0.70–0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.}, tags = {8.4, 8.44}, web_url = {http://www.nature.com/articles/s41598-021-92475-7}, web_url_date = {2022-09-13}, language = {en}, ISSN = {2045-2322}, DOI = {10.1038/s41598-021-92475-7}, author = {Lichtner, Gregor and Balzer, Felix and Haufe, Stefan and Giesa, Niklas and Schiefenh{\"o}vel, Fridtjof and Schmieding, Malte and Jurth, Carlo and Kopp, Wolfgang and Akalin, Altuna and Schaller, Stefan J. and Weber-Carstens, Steffen and Spies, Claudia and von Dincklage, Falk} } @Inproceedings { ali_hashemi_efficient_2021, title = {Efficient hierarchical {Bayesian} inference for spatio-temporal regression models in neuroimaging}, year = {2021}, volume = {34}, pages = {24855--24870}, tags = {8.4, 8.44}, web_url = {https://proceedings.neurips.cc/paper/2021/file/d03a857a23b5285736c4d55e0bb067c8-Paper.pdf}, editor = {{M. Ranzato} and {A. Beygelzimer} and {Y. Dauphin} and {P.S. Liang} and {J. Wortman Vaughan}}, publisher = {Curran Associates, Inc.}, booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, author = {Hashemi, {Ali} and Gao, Yijing and Cai, Chang and Ghosh, Sanjay and M{\textbackslash}''{u}ller, Klaus-Robert and Nagarajan, Srikantan and Haufe, Stefan} }