• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 115 matches

• 2021
• 
  Alessandro Fanfarillo, Behrooz Roozitalab, Weiming Hu, Guido Cervone:
  Probabilistic forecasting using deep generative models. GeoInformatica 25(1): 127-147 (2021)
• 
  Koffi Eddy Ihou, Nizar Bouguila, Wassim Bouachir:
  Efficient integration of generative topic models into discriminative classifiers using robust probabilistic kernels. Pattern Anal. Appl. 24(1): 217-241 (2021)
• 
  Mahdi Khodayar, Jianhui Wang:
  Probabilistic Time-Varying Parameter Identification for Load Modeling: A Deep Generative Approach. IEEE Trans. Ind. Informatics 17(3): 1625-1636 (2021)
• 2020
• 
  Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura:
  Spatial concept-based navigation with human speech instructions via probabilistic inference on Bayesian generative model. Adv. Robotics 34(19): 1213-1228 (2020)
• 
  Gianni Costa, Riccardo Ortale:
  Integrating overlapping community discovery and role analysis: Bayesian probabilistic generative modeling and mean-field variational inference. Eng. Appl. Artif. Intell. 89: 103437 (2020)
• 
  Tadahiro Taniguchi, Tomoaki Nakamura, Masahiro Suzuki, Ryo Kuniyasu, Kaede Hayashi, Akira Taniguchi, Takato Horii, Takayuki Nagai:
  Neuro-SERKET: Development of Integrative Cognitive System Through the Composition of Deep Probabilistic Generative Models. New Gener. Comput. 38(1): 23-48 (2020)
• 
  Praful Agrawal, Ross T. Whitaker, Shireen Y. Elhabian:
  An Optimal, Generative Model for Estimating Multi-Label Probabilistic Maps. IEEE Trans. Medical Imaging 39(7): 2316-2326 (2020)
• 
  Kartik Goyal, Chris Dyer, Christopher Warren, Max G'Sell, Taylor Berg-Kirkpatrick:
  A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing. ACL 2020: 2954-2960
• 
  Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura:
  Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model. CoRR abs/2002.07381 (2020)
• 
  Kartik Goyal, Chris Dyer, Christopher Warren, Max G'Sell, Taylor Berg-Kirkpatrick:
  A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing. CoRR abs/2005.01646 (2020)
• 
  Maximilian Rixner, Phaedon-Stelios Koutsourelakis:
  A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables. CoRR abs/2006.01789 (2020)
• 
  Xin Sun, Chi Zhang, Guosheng Lin, Keck Voon Ling:
  Open Set Recognition with Conditional Probabilistic Generative Models. CoRR abs/2008.05129 (2020)
• 
  Maurice Frank, Maximilian Ilse:
  Problems using deep generative models for probabilistic audio source separation. CoRR abs/2011.01761 (2020)
• 
  Francisco McGee, Quentin Novinger, Ronald M. Levy, Vincenzo Carnevale, Allan Haldane:
  Generative Capacity of Probabilistic Protein Sequence Models. CoRR abs/2012.02296 (2020)
• 2019
• 
  Zhiling Luo, Ling Liu, Jianwei Yin, Ying Li, Zhaohui Wu:
  Latent Ability Model: A Generative Probabilistic Learning Framework for Workforce Analytics. IEEE Trans. Knowl. Data Eng. 31(5): 923-937 (2019)
• 
  Yi Liu, Yunchun Li, Honggang Zhou, Hailong Yang, Wei Li:
  Generative Model for Probabilistic Inference. DASC/PiCom/DataCom/CyberSciTech 2019: 803-810
• 
  Cao Shi, Ye Tao, Can Xu, Xiaodong Wang, Yanzhe Wang, Zihao Wang:
  Sentiment Analysis of Home Appliance Comment Based on Generative Probabilistic Model. DSC 2019: 318-321
• 
  Gregor Pavlin, Anne-Laure Jousselme, Johan Pieter de Villiers, Paulo C. G. Costa, Kathryn B. Laskey, Franck Mignet, Alta de Waal:
  Online System Evaluation and Learning of Data Source Models: a Probabilistic Generative Approach. FUSION 2019: 1-10
• 
  Yuhuan Lu, Zhaocheng He, Liangkui Luo:
  Learning trajectories as words: a probabilistic generative model for destination prediction. MobiQuitous 2019: 464-472
• 
  Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell, Isabelle Augenstein:
  A Probabilistic Generative Model of Linguistic Typology. NAACL-HLT (1) 2019: 1529-1540
• 
  Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell, Isabelle Augenstein:
  A Probabilistic Generative Model of Linguistic Typology. CoRR abs/1903.10950 (2019)
• 
  Akira Kinose, Tadahiro Taniguchi:
  Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Generative Model. CoRR abs/1907.02140 (2019)
• 
  Vikash Singh, Pietro Liò:
  Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction. CoRR abs/1907.05628 (2019)
• 
  Ruofeng Wen, Kari Torkkola:
  Deep Generative Quantile-Copula Models for Probabilistic Forecasting. CoRR abs/1907.10697 (2019)
• 
  Alessandro Fanfarillo, Behrooz Roozitalab, Weiming Hu, Guido Cervone:
  Probabilistic Forecasting using Deep Generative Models. CoRR abs/1909.11865 (2019)
• 
  Tadahiro Taniguchi, Tomoaki Nakamura, Masahiro Suzuki, Ryo Kuniyasu, Kaede Hayashi, Akira Taniguchi, Takato Horii, Takayuki Nagai:
  Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models. CoRR abs/1910.08918 (2019)
• 2018
• 
  Xiaofeng Yuan, Zhiwen Chen, Yalin Wang:
  Probabilistic Nonlinear Soft Sensor Modeling Based on Generative Topographic Mapping Regression. IEEE Access 6: 10445-10452 (2018)
• 
  Sebastián Moreno, Joseph J. Pfeiffer III, Jennifer Neville:
  Scalable and exact sampling method for probabilistic generative graph models. Data Min. Knowl. Discov. 32(6): 1561-1596 (2018)
• 
  Claudia Blaiotta, Patrick Freund, M. Jorge Cardoso, John Ashburner:
  Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. NeuroImage 166: 117-134 (2018)
• 
  Li Liu, Shu Wang, Bin Hu, Qingyu Qiong, Junhao Wen, David S. Rosenblum:
  Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition. Pattern Recognit. 81: 545-561 (2018)

skipping 85 more matches