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arXiv 2017年

作者： Doran, Derek Schulz, Sarah Besold, Tarek R. Dept. of Computer Science & Engineering Kno.e.sis Research Center Wright State University DaytonOH United States Institute for Natural Language Processing University of Stuttgart Stuttgart Germany Department of Computer Science City University of London London United Kingdom

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algorithmic mechanisms;interpretable systems where users can mathematically analyze its algorithmic mechanisms;and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process. Copyright © 2017, The Authors. All rights reserved.

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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Context-dependent point process models for keyword search and detection-based ASR

Context-dependent point process models for keyword search an...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Chunxi Liu Aren Jansen Sanjeev Khudanpur Department of Electrical and Computer Engineering Center for Language and Speech Processing Human Language Technology Center of Excellence The Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479999897

The point process model (PPM) for keyword search (KWS) is a whole-word parametric approach that characterizes each query type by the timing of phonetic events observed during its production. In this paper, we first extend the PPM modeling framework to operate on context-dependent phonetic event patterns instead of monophone patterns considered in the past, which provides significant KWS improvements. Second, we use the context-dependent PPMs to drive a detection-based speech recognition architecture thats runs parallel word detectors covering the whole vocabulary and uses the independent detections to construct lattices that can be used for both KWS indexing and LVCSR decoding. This strategy produces significant improvements over the original PPM KWS framework and provides an encouraging first attempt at PPM-based LVCSR.

关键词： Hidden Markov models Lattices Acoustics Indexes Context modeling Vocabulary Computational modeling

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Intra-topic variability normalization based on linear projection for topic classification 15

Intra-topic variability normalization based on linear projec...

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15th Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL HLT 2016

作者： Liu, Quan Guo, Wu Ling, Zhen-Hua Jiang, Hui Hu, Yu National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China HefeiAnhui China Department of Electrical Engineering and Computer Science York University 4700 Keele Street TorontoONM3J 1P3 Canada iFLYTEK Research Hefei China

ISBN: (纸本)9781941643914

This paper proposes a variability normalization algorithm to reduce the variability between intra-topic documents for topic classification. Firstly, an optimization problem is constructed based on linear variability removable assumption. Secondly, a new feature space for document representation is found by solving the optimization problem with kernel principle component analysis (KPCA). Finally, effective feature transformation is taken through linear projection. As for experiments, state-of-the-art SVM and KNN algorithm are adopted for topic classification respectively. Experimental results on a free-style conversational corpus show that the proposed variability normalization algorithm for topic classification achieves 3.8% absolute improvement for micro-F1 measure. ©2016 Association for Computational Linguistics.

关键词： Linear transformations

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A Study of Statistical Machine Translation Methods for Under Resourced languages

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Procedia computer science 2016年 81卷 250-257页

作者： Win Pa Pa Ye Kyaw Thu Andrew Finch Eiichiro Sumita Natural Language Processing Lab. University of Computer Studies Yangon Myanmar Advanced Speech Translation Research and Development Promotion Center National Institute of Information and Communications Technology Kyoto Japan Language and Speech Science Research Lab. Department of Applied Mathematics Global Information and Telecommunication Institute Waseda University Tokyo Japan

This paper contributes an empirical study of the application of ﬁve state-of-the-art machine translation to the trans- lation of low-resource languages. The methods studied were phrase-based, hierarchical phrase-based, the operational sequence model, string-to-tree, tree-to-string statistical machine translation methods between English (en) and the under resourced languages Lao (la), Myanmar (mm), Thai (th) in both directions. The performance of the machine translation systems was automatically measured in terms of BLEU and RIBES for all experiments. Our main ﬁndings were that the phrase-based SMT method generally gave the highest BLEU scores. This was counter to expectations, and we believe indicates that this method may be more robust to limitations on the data set size. However, when evaluated with RIBES, the best scores came from methods other than phrase-based SMT, indicating that the other methods were able to handle the word re-ordering better even under the constraint of limited data. Our study achieved the highest reported results on the data sets for all translation language pairs.

关键词： Machine translation Under resourced languages Phrase-based Hierarchical Phrase-based Operation Sequence Model Syntax-based

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Neuromorphic sampling on the SpiNNaker and parallella chip multiprocessors 7

Neuromorphic sampling on the SpiNNaker and parallella chip m...

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7th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2016

作者： Mendat, Daniel R. Chin, Sang Furber, Steve Andreou, Andreas G. Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States School of Computer Science University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9781467378352

We present a bio-inspired, hardware/software architecture to perform Markov Chain Monte Carlo sampling on probabilistic graphical models using energy aware hardware. We have developed algorithms and programming data flows for two recently developed multiprocessor architectures, the SpiNNaker and Parallella. We employ a neurally inspired sampling algorithm that abstracts the functionality of neurons in a biological network and exploits the neural dynamics to implement the sampling process. This algorithm maps nicely on the two hardware systems. Speedups as high as 1000 fold are achieved when performing inference using this approach, compared to algorithms running on traditional engineering workstations. © 2016 IEEE.

关键词： Markov processes

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Efficient, dense, object-based segmentation from RGBD video

Efficient, dense, object-based segmentation from RGBD video

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mahsa Ghafarianzadeh Matthew B. Blaschko Gabe Sibley Department of Computer Science University of Colorado-Boulder Boulder CO USA Center for Processing Speech and Images KU Leuven Leuven Belgium

ISBN: (纸本)9781467380270

Spatio-temporal cues offer a rich source of information for inferring structural and semantic scene properties. A particularly useful representation in computer vision is a spatio-temporal video segmentation. Together with motion, knowledge of depth can substantially improve superpixel segmentation. In this work we present a novel framework for spatio-temporal segmentation from RGBD video. The method employs both low-level (intensity, color) and high-level (deformable parts model) appearance features. Motion is incorporated through the use of optical flow to construct the temporal connections in the graph Laplacian. Depth cues are incorporated in the similarity metric to provide an informative cue for object boundaries at depth disparities. Naïve application of spectral clustering to dense spatio-temporal graphs leads to a high computational cost that is typically addressed through the use of GPUs or computer clusters. By contrast, we build upon a recently proposed Nyström approximation strategy for spatio-temporal clustering that enables computation on a single core. We further explore structured local connectivity patterns to give high performance at low computational cost. Also we propose a novel context-aware aggregation method that uses a deformable parts model to group the detected parts of the object as a single segment with an accurate boundary. Detailed experiments on the NYU Depth Dataset and TUM RGBD Dataset is performed to compare against previous large-scale graph-based spatio-temporal segmentation techniques which shows the substantial performance advantages of our framework.

关键词： Image segmentation Motion segmentation Image color analysis Three-dimensional displays Optical imaging Cameras Computational modeling

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Neuromorphic sampling on the SpiNNaker and parallella chip multiprocessors

Neuromorphic sampling on the SpiNNaker and parallella chip m...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Daniel R. Mendat Sang Chin Steve Furber Andreas G. Andreou Center for Language and Speech Processing Johns Hopkins University Baltimore Maryland Applied Physics Laboratory Johns Hopkins University Laurel Maryland School of Computer Science The University of Manchester Manchester UK

关键词： Neurons Markov processes MATLAB Graphical models Hardware Bayes methods Inference algorithms

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SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

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9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Relative entropy normalized Gaussian supervector for speech emotion recognition using kernel extreme learning machine

Relative entropy normalized Gaussian supervector for speech ...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Ruru Li Dali Yang Xinxing Li Renyu Wang Mingxing Xu Thomas Fang Zheng School of Computer Science Beijing Information Science & Technology University China Tsinghua University Beijing Beijing CN Center for Speech and Language Technologies Tsinghua University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781509024018

speech emotion recognition is a challenging and significant task. On the one hand, the emotion features need to be robust enough to capture the emotion information, and while on the other, machine learning algorithms need to be insensitive to model the utterance. In this paper, we presented a novel framework of speech emotion recognition to address the two above-mentioned challenges. Relative Entropy based Normalization (REN) was proposed to normalize the supervectors of Gaussian Mixture Model-Universal Background Model (GMM-UBM) as the features to emotions. The Kernel Extreme Learning Machine (KELM) was adopted as the classifier to identify the emotion represented by the normalized supervectors. Experimental results on the EMR_1309 corpus showed the proposed framework outperformed the state-of-the-art i-vector based systems.

关键词： speech Entropy speech recognition Feature extraction Emotion recognition Kernel Covariance matrices

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