Deterministic Decoding for Discrete Data in Variational Autoencoders
Polykovskiy D., Vetrov D.
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108
Eduard Gorbunov, Hanzely F., Richtarik P. Issue 108. , PMLR, 2020
, , et al., , in : WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining. : Association for Computing Machinery (ACM), 2020. P. 528-536.
Added: October 28, 2020
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation
, , et al., , in : Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. : [б.и.], 2021. P. 11426-11435.
We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only. Our method combines a neural network encoder with a tensor train decomposition to learn a low-rank latent encoding, coupled with cross-approximation (CA) to learn the representation through a subset of the original samples. CA ...
Added: November 3, 2021
Solving black-box optimization challenge via learning search space partition for local bayesian optimization
, , et al., , in : Proceedings of Machine Learning Research. Vol. 133: Proceedings of the NeurIPS 2020: Competition and Demonstration Track.: PMLR, 2021. P. 77-85.
Added: October 11, 2021