Jiawei He
Jiawei He
Ph.D in School of Computing Science, Simon Fraser University
Verified email at sfu.ca - Homepage
Title
Cited by
Cited by
Year
Probabilistic Video Generation using Holistic Attribute Control
J He, A Lehrmann, J Marino, G Mori, L Sigal
European Conference on Computer Vision, 2018
352018
Lifelong gan: Continual learning for conditional image generation
M Zhai, L Chen, F Tung, J He, M Nawhal, G Mori
Proceedings of the IEEE International Conference on Computer Vision, 2759-2768, 2019
242019
Layoutvae: Stochastic scene layout generation from a label set
AA Jyothi, T Durand, J He, L Sigal, G Mori
Proceedings of the IEEE International Conference on Computer Vision, 9895-9904, 2019
182019
Generic Tubelet Proposals for Action Localization
J He, MS Ibrahim, Z Deng, G Mori
Winter Conference on Applications of Computer Vision, 2018
182018
A Variational Auto-Encoder Model for Stochastic Point Processes
N Mehrasa, A Abdu Jyothi, T Durand, J He, L Sigal, G Mori
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
112019
Variational Autoencoders with Jointly Optimized Latent Dependency Structure
J He, Y Gong, J Marino, G Mori, A Lehrmann
International Conference on Learning Representations (ICLR), 2019
52019
Object grounding via iterative context reasoning
L Chen, M Zhai, J He, G Mori
Proceedings of the IEEE International Conference on Computer Vision†…, 2019
32019
Variational selective autoencoder
Y Gong, H Hajimirsadeghi, J He, M Nawhal, T Durand, G Mori
Symposium on Advances in Approximate Bayesian Inference, 1-17, 2020
22020
Informed Priors for Deep Representation Learning
J BŁtepage, J He, C Zhang, L Sigal, G Mori, S Mandt
Symposium on Advances in Approximate Bayesian Inference, 0
2*
Point Process Flows
N Mehrasa, R Deng, MO Ahmed, B Chang, J He, T Durand, M Brubaker, ...
arXiv preprint arXiv:1910.08281, 2019
12019
System and method for generative model for stochastic point processes
N Mehrasa, AA Jyothi, T Durand, J He, M Gregory, M Ahmed, M Brubaker
US Patent App. 16/685,327, 2020
2020
Improving Sequential Latent Variable Models with Autoregressive Flows
J Marino, L Chen, J He, S Mandt
Symposium on Advances in Approximate Bayesian Inference, 1-16, 2020
2020
Theoretical and applicational advances in variational autoencoders
J He
Applied Sciences: School of Computing Science, 2019
2019
Arbitrarily-conditioned Data Imputation
M Carvalho, T Durand, J He, N Mehrasa, G Mori
2019
Learning from Partially-Observed Multimodal Data with Variational Autoencoders
Y Gong, H Hajimirsadeghi, J He, M Nawhal, T Durand, G Mori
2019
LayoutVAE: Stochastic Scene Layout Generation From a Label Set
A Abdu Jyothi, T Durand, J He, L Sigal, G Mori
arXiv, arXiv: 1907.10719, 2019
2019
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation
M Zhai, L Chen, J He, M Nawhal, F Tung, G Mori
Variational Latent Dependency Learning
J He, Y Gong, J Marino, G Mori, A Lehrmann
Bayesian Deep Learning Workshop (NeurIPS '18), 0
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Articles 1–18