• 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 35 matches

• 2024
• 
  Thomas Gonzalez, Antoine Blais, Nicolas Couëllan, Christian Ruiz:
  Distributional loss for convolutional neural network regression and application to parameter estimation in satellite navigation signals. Expert Syst. Appl. 255: 124682 (2024)
• 
  Shuo Hu, Sien Zhou, Jinbo Lu, Hui Yu:
  Flexible Dual-Branch Siamese Network: Learning Location Quality Estimation and Regression Distribution for Visual Tracking. IEEE Trans. Comput. Soc. Syst. 11(1): 1451-1459 (2024)
• 
  Shunyu Wu, Jingcheng Wang, Haotian Xu, Shangwei Zhao, Jiahui Xu:
  CritiCoder: An End-to-End Uncertain Regression Network for Robust Macroscopic Pressure Models in Water Distribution Systems. IEEE Trans. Comput. Soc. Syst. 11(2): 2222-2233 (2024)
• 2023
• 
  Nien-Che Yang, Abhilash Sen:
  Parameter estimation in unbalanced three-phase distribution lines using general regression neural networks with inconsistent data handling capacity. Appl. Soft Comput. 133: 109936 (2023)
• 
  Di Zhu, Yu Liu, Xin Yao, Manfred M. Fischer:
  Correction to: Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica 27(3): 641-642 (2023)
• 
  Lin Chen, Xin Luo:
  Tensor Distribution Regression Based on the 3D Conventional Neural Networks. IEEE CAA J. Autom. Sinica 10(7): 1628-1630 (2023)
• 
  David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp F. M. Baumann, Lucas Kook, Nadja Klein, Christian L. Müller:
  deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. J. Stat. Softw. 105(2) (2023)
• 
  Ali Harakeh, Jordan Sir Kwang Hu, Naiqing Guan, Steven L. Waslander, Liam Paull:
  Estimating Regression Predictive Distributions with Sample Networks. AAAI 2023: 7830-7838
• 
  Ralf Lübben, Amr Rizk:
  TAILING: Tail Distribution Forecasting of Packet Delays Using Quantile Regression Neural Networks. ICC 2023: 377-383
• 
  José A. López, Felipe Morales-Osorio, Maximiliano Lara, Jonás Velasco, Claudia N. Sánchez:
  Bayesian Network-Based Multi-objective Estimation of Distribution Algorithm for Feature Selection Tailored to Regression Problems. MICAI (1) 2023: 309-326
• 
  Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou:
  Learning Theory of Distribution Regression with Neural Networks. CoRR abs/2307.03487 (2023)
• 2022
• 
  Di Zhu, Yu Liu, Xin Yao, Manfred M. Fischer:
  Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica 26(4): 645-676 (2022)
• 
  Shruti Sharma, Santanu Chaudhury, Jayadeva:
  Block Sparse Variational Bayes Regression Using Matrix Variate Distributions With Application to SSVEP Detection. IEEE Trans. Neural Networks Learn. Syst. 33(1): 351-365 (2022)
• 
  Jun Wu, Jingrui He, Sheng Wang, Kaiyu Guan, Elizabeth A. Ainsworth:
  Distribution-Informed Neural Networks for Domain Adaptation Regression. NeurIPS 2022
• 
  Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu:
  Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis. NeurIPS 2022
• 
  Thomas Gonzalez, Antoine Blais, Nicolas Couëllan, Christian Ruiz:
  Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation. CoRR abs/2206.01473 (2022)
• 
  Ali Harakeh, Jordan S. K. Hu, Naiqing Guan, Steven L. Waslander, Liam Paull:
  Estimating Regression Predictive Distributions with Sample Networks. CoRR abs/2211.13724 (2022)
• 2021
• 
  Eunho Koo, Hyungjun Kim:
  Empirical strategy for stretching probability distribution in neural-network-based regression. Neural Networks 140: 113-120 (2021)
• 
  David Rügamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Nadja Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Christian L. Müller:
  deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. CoRR abs/2104.02705 (2021)
• 2020
• 
  Connie Khor Li Kou, Hwee Kuan Lee, Teck Khim Ng, Jorge Sanz:
  Theoretical and experimental analysis on the generalizability of distribution regression network. Neurocomputing 401: 257-270 (2020)
• 
  Muhammad Umar Afzaal, Intisar Ali Sajjad, Muhammad Faisal Nadeem Khan, Saaqib Haroon, Salman Amin, Rui Bo, Waqas ur Rehman:
  Inter-temporal characterization of aggregate residential demand based on Weibull distribution and generalized regression neural networks for scenario generations. J. Intell. Fuzzy Syst. 39(3): 4491-4503 (2020)
• 
  Eunho Koo, Hyungjun Kim:
  Empirical Strategy for Stretching Probability Distribution in Neural-network-based Regression. CoRR abs/2009.03534 (2020)
• 2019
• 
  Radhia Fazai, Majdi Mansouri, Kamal Abodayeh, Vicenç Puig, M.-I. Noori Raouf, Hazem N. Nounou, Mohamed N. Nounou:
  Multiscale Gaussian process regression-based generalized likelihood ratio test for fault detection in water distribution networks. Eng. Appl. Artif. Intell. 85: 474-491 (2019)
• 
  Rajakumaran Gayathri, Neelanarayanan Venkataraman:
  Identification of regression function and distribution model for denial of service attack in Second Life online community using simple network management protocol. Int. J. Web Based Communities 15(3): 225-237 (2019)
• 
  F. Akhlaghi, M. Khakbiz, A. Rezaii Bazazz:
  Evolution of the size distribution of Al-B4C nano-composite powders during mechanical milling: a comparison of experimental results with artificial neural networks and multiple linear regression models. Neural Comput. Appl. 31(S-2): 1145-1154 (2019)
• 
  Connie Khor Li Kou, Hwee Kuan Lee, Teck Khim Ng:
  A compact network learning model for distribution regression. Neural Networks 110: 199-212 (2019)
• 
  Ildeberto Santos-Ruiz, Francisco-Ronay López-Estrada, Vicenç Puig, Joaquim Blesa:
  Estimation of Node Pressures in Water Distribution Networks by Gaussian Process Regression. SysTol 2019: 50-55
• 2018
• 
  Connie Kou, Hwee Kuan Lee, Teck Khim Ng:
  Distribution Regression Network. CoRR abs/1804.04775 (2018)
• 2017
• 
  Lei Luo, Jian Yang, Jianjun Qian, Ying Tai, Gui-Fu Lu:
  Robust Image Regression Based on the Extended Matrix Variate Power Exponential Distribution of Dependent Noise. IEEE Trans. Neural Networks Learn. Syst. 28(9): 2168-2182 (2017)
• 2016
• 
  Haoyong Chen, Pascal Dieu Seul Assala, Yongzhi Cai, Ping Yang:
  Intelligent Transient Overvoltages Location in Distribution Systems Using Wavelet Packet Decomposition and General Regression Neural Networks. IEEE Trans. Ind. Informatics 12(5): 1726-1735 (2016)

skipping 5 more matches