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Computational intelligence
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#	Item
151	Needs Assessment for the Design of Information Synthesis Visual Analytics Tools Anthony C. Robinson GeoVISTA Center, The Pennsylvania State University {} Abstract Information synthesis is a key portion o Add to Reading List Source URL: www.geovista.psu.edu Language: English - Date: 2016-07-01 14:44:24 Computational science Computer graphics Infographics Visualization Science Scientific modeling Communication design Visual analytics Pacific Northwest National Laboratory Data analysis Intelligence analysis Information visualization
152	Attend, Infer, Repeat: Fast Scene Understanding with Deep Generative Models S. M. Ali Eslami David Szepesvari Add to Reading List Source URL: juxi.net Language: English - Date: 2016-06-17 11:44:38 Artificial neural networks Statistics Artificial intelligence Applied mathematics Cybernetics Computational neuroscience Computational statistics Deep learning Graphical models Bayesian network Pattern recognition Computer vision
153	From Words to Behaviour via Semantic Networks Armand S. Rotaru () Gabriella Vigliocco () Faculty of Brain Sciences, University College London, Add to Reading List Source URL: www.stefanfrank.info Language: English - Date: 2016-05-14 09:09:53 Semantics Cognitive science Cognition Computational linguistics Linguistics Semantic memory Psycholinguistics Natural language processing Artificial intelligence Semantic similarity Distributional semantics Semantic network
154	Sequential Decision Making with Untrustworthy Service Providers W. T. Luke Teacy, Georgios Chalkiadakis, Alex Rogers and Nicholas R. Jennings Electronics and Computer Science,University of Southampton Southampton, SO17 1 Add to Reading List Source URL: www.intelligence.tuc.gr Language: English - Date: 2008-02-08 15:13:13 Cognitive science Artificial intelligence Cognition Philosophy Game theory Computational trust Computer access control Key management Software agent Reinforcement learning Practical reason Trust
155	The rise of automatic feature-generation techniques, including deep learning, has the potential to greatly enlarge the pool of machinelearning users. Such methods require large labeled training sets to obtain high-qualit Add to Reading List Source URL: mmds-data.org Language: English - Date: 2016-06-23 15:50:48 Education Educational psychology Technology Computational neuroscience Formal sciences Science and technology Distance education Educational technology Information technology Deep learning Artificial intelligence
156	arXiv:1602.03935v2 [cs.CV] 21 JunFace Attribute Prediction Using Off-the-Shelf CNN Features Yang Zhong Josephine Sullivan Add to Reading List Source URL: arxiv.org Language: English - Date: 2016-06-21 21:29:10 Computational neuroscience Artificial neural networks Applied mathematics Cybernetics Neuroscience Computer vision Convolutional neural network Deep learning Receptive field Artificial intelligence Object Process Methodology
157	Recommender systems @ LIAAD in collaboration with other centres of INESC TEC Early projects }  Add to Reading List Source URL: webesitix.inescporto.pt Language: English Natural language processing Information science Information retrieval Data mining Artificial intelligence Computational linguistics Recommender system Automatic summarization Text mining Document clustering Temporal information retrieval Document classification
158	No Fuzzy Creep! A Clustering Algorithm for Controlling Arbitrary Node Movement Francis Harvey EPFL-IGEO-SIRS GR-Ecublens Add to Reading List Source URL: mapcontext.com Language: English - Date: 2008-08-29 22:37:25 Computing Artificial intelligence Data mining Cluster analysis Geostatistics Computer cluster Computational intelligence Neuro-fuzzy
159	A Machine Learning Approach for Filtering Monte Carlo Noise Add to Reading List Source URL: cvc.ucsb.edu Language: English - Date: 2015-05-13 18:18:10 Artificial intelligence Statistics Computational neuroscience Machine learning Artificial neural networks Computational statistics Mathematical psychology Noise reduction Backpropagation Bilateral filter Mixture model Convolutional neural network
160	COMBINING I-VECTOR REPRESENTATION AND STRUCTURED NEURAL NETWORKS FOR RAPID ADAPTATION Chunyang Wu, Penny Karanasou, Mark J.F. Gales Cambridge University Engineering Dept, Trumpington St., Cambridge, CB2 1PZ U.K. Email: { Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2016-04-05 12:13:29 Machine learning Automatic identification and data capture Artificial intelligence Humancomputer interaction Markov models Artificial neural networks Computational neuroscience Computer accessibility Speech recognition Perceptron Speaker recognition Hidden Markov model

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