• 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 26 matches

• 2023
• 
  Jaehyoung Hong, Hyonho Chun:
  A prediction model for healthcare time-series data with a mixture of deep mixed effect models using Gaussian processes. Biomed. Signal Process. Control. 84: 104753 (2023)
• 
  Ghazaleh Niknam, Soheila Molaei, Hadi Zare, David A. Clifton, Shirui Pan:
  Graph representation learning based on deep generative gaussian mixture models. Neurocomputing 523: 157-169 (2023)
• 
  Lucas Kock, Nadja Klein, David J. Nott:
  Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. 33(1): 24 (2023)
• 
  Shasha Yuan, Xiang Liu, Junliang Shang, Jin-Xing Liu, Juan Wang, Weidong Zhou:
  Automatic Seizure Detection Using Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and Improved Deep Forest Learning. IEEE J. Biomed. Health Informatics 27(3): 1386-1396 (2023)
• 
  Tian-Yi Zhou, Xiaoming Huo:
  Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks. CoRR abs/2308.08030 (2023)
• 2022
• 
  Lucas Kock, Nadja Klein, David J. Nott:
  Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. 32(5): 70 (2022)
• 
  Vlad Winter, Or Dinari, Oren Freifeld:
  Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models - and a Deep-learning Solution. AISTATS 2022: 1432-1456
• 
  Robin Fuchs, Denys Pommeret, Samuel Stocksieker:
  MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models. LOD (1) 2022: 211-222
• 
  Vlad Winter, Or Dinari, Oren Freifeld:
  Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models - and a Deep-learning Solution. CoRR abs/2203.13661 (2022)
• 
  Huajian Fang, Timo Gerkmann:
  Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models. CoRR abs/2212.04831 (2022)
• 2021
• 
  Mohamed Sayah, Djillali Guebli, Noureddine Zerhouni, Zeina Al Masry:
  Deep LSTM Enhancement for RUL Prediction Using Gaussian Mixture Models. Autom. Control. Comput. Sci. 55(1): 15-25 (2021)
• 
  Alexander Gepperth, Benedikt Pfülb:
  Image Modeling with Deep Convolutional Gaussian Mixture Models. IJCNN 2021: 1-9
• 
  Alexander Gepperth, Benedikt Pfülb:
  Image Modeling with Deep Convolutional Gaussian Mixture Models. CoRR abs/2104.12686 (2021)
• 2020
• 
  Wentao Yuan, Benjamin Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, Jan Kautz:
  DeepGMR: Learning Latent Gaussian Mixture Models for Registration. ECCV (5) 2020: 733-750
• 
  Natalia A. Tomashenko, Yuri Y. Khokhlov, Yannick Estève:
  Exploring Gaussian mixture model framework for speaker adaptation of deep neural network acoustic models. CoRR abs/2003.06894 (2020)
• 
  Amine Echraibi, Joachim Flocon-Cholet, Stéphane Gosselin, Sandrine Vaton:
  On the Variational Posterior of Dirichlet Process Deep Latent Gaussian Mixture Models. CoRR abs/2006.08993 (2020)
• 
  Wentao Yuan, Benjamin Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, Jan Kautz:
  DeepGMR: Learning Latent Gaussian Mixture Models for Registration. CoRR abs/2008.09088 (2020)
• 2019
• 
  Cinzia Viroli, Geoffrey J. McLachlan:
  Deep Gaussian mixture models. Stat. Comput. 29(1): 43-51 (2019)
• 
  Masanao Matsumoto, Naoki Saito, Takahiro Ogawa, Miki Haseyama:
  Chronic Gastritis Detection from Gastric X-ray Images via Deep Autoencoding Gaussian Mixture Models. LifeTech 2019: 231-232
• 2017
• 
  Natalia A. Tomashenko:
  Speaker adaptation of deep neural network acoustic models using Gaussian mixture model framework in automatic speech recognition systems. (Utilisation de modèles gaussiens pour l'adaptation au locuteur de réseaux de neurones profonds dans un contexte de modélisation acoustique pour la reconnaissance de la parole). University of Maine, Le Mans, France, 2017
• 
  Ivan J. Tashev, Zhong-Qiu Wang, Keith W. Godin:
  Speech emotion recognition based on Gaussian Mixture Models and Deep Neural Networks. ITA 2017: 1-4
• 
  Cinzia Viroli, Geoffrey J. McLachlan:
  Deep Gaussian Mixture Models. CoRR abs/1711.06929 (2017)
• 2016
• 
  Natalia A. Tomashenko, Yuri Y. Khokhlov, Yannick Estève:
  On the Use of Gaussian Mixture Model Framework to Improve Speaker Adaptation of Deep Neural Network Acoustic Models. INTERSPEECH 2016: 3788-3792
• 2014
• 
  Yan Huang, Dong Yu, Chaojun Liu, Yifan Gong:
  A comparative analytic study on the Gaussian mixture and context dependent deep neural network hidden Markov models. INTERSPEECH 2014: 1895-1899
• 
  Aäron van den Oord, Benjamin Schrauwen:
  Factoring Variations in Natural Images with Deep Gaussian Mixture Models. NIPS 2014: 3518-3526
• 2013
• 
  Xin Lei, Hui Lin, Georg Heigold:
  Deep neural networks with auxiliary Gaussian mixture models for real-time speech recognition. ICASSP 2013: 7634-7638