Zhenwen Dai
Zhenwen Dai
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Citado por
Citado por
Variational Information Distillation for Knowledge Transfer
S Ahn, SX Hu, A Damianou, ND Lawrence, Z Dai
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019
Batch Bayesian Optimization via Local Penalization
J González, Z Dai, P Hennig, ND Lawrence
International Conference on Artificial Intelligence and Statistics, 2015
Variational Auto-encoded Deep Gaussian Processes
Z Dai, A Damianou, J González, N Lawrence
International Conference on Learning Representations (ICLR), 2015
GPy: A Gaussian process framework in python
GPy, 2012
Recurrent Gaussian Processes
CLC Mattos, Z Dai, A Damianou, J Forth, GA Barreto, ND Lawrence
International Conference on Learning Representations (ICLR), 2015
Preferential Bayesian Optimization
J Gonzalez, Z Dai, A Damianou, ND Lawrence
International Conference on Machine Learning, 2017
Structured variationally auto-encoded optimization
X Lu, J Gonzalez, Z Dai, ND Lawrence
International conference on machine learning, 3267-3275, 2018
Meta-surrogate benchmarking for hyperparameter optimization
A Klein, Z Dai, F Hutter, N Lawrence, J Gonzalez
Advances in Neural Information Processing Systems 32, 2019
Gaussian process models with parallelization and GPU acceleration
Z Dai, A Damianou, J Hensman, N Lawrence
arXiv preprint arXiv:1410.4984, 2014
Auto-differentiating linear algebra
M Seeger, A Hetzel, Z Dai, E Meissner, ND Lawrence
arXiv preprint arXiv:1710.08717, 2017
GPyOpt: a Bayesian optimization framework in Python
J González, Z Dai
Accessed, 2016
Data-driven mode identification and unsupervised fault detection for nonlinear multimode processes
B Wang, Z Li, Z Dai, N Lawrence, X Yan
IEEE Transactions on Industrial Informatics 16 (6), 3651-3661, 2019
Deep recurrent Gaussian processes for outlier-robust system identification
CLC Mattos, Z Dai, A Damianou, GA Barreto, ND Lawrence
Journal of Process Control 60, 82-94, 2017
Polygonal light source estimation
D Schnieders, KYK Wong, Z Dai
Asian conference on computer vision, 96-107, 2009
Efficient modeling of latent information in supervised learning using gaussian processes
A Lopez, Z Dai, ND Lawrence
Advances in Neural Information Processing Systems 30 (NIPS 2017) pre …, 2017
Autonomous Document Cleaning—A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts
Z Dai, J Lucke
IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (10), 1950 …, 2014
GP-select: Accelerating EM using adaptive subspace preselection
JA Shelton, J Gasthaus, Z Dai, J Lücke, A Gretton
Neural Computation 29 (8), 2177-2202, 2017
Intrinsic Gaussian processes on complex constrained domains
M Niu, P Cheung, L Lin, Z Dai, N Lawrence, D Dunson
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2019
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Z Dai, MA Álvarez, ND Lawrence
Advances in Neural Information Processing Systems, 2017
What are the invariant occlusive components of image patches? a probabilistic generative approach
Z Dai, G Exarchakis, J Lücke
Advances in neural information processing systems 26, 2013
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