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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Inexact Tensor Methods And Their Application To Stochastic Convex Optimization, Artem Agafonov, Dmitry Kamzolov, Pavel Dvurechensky, Alexander Gasnikov, Martin Takac
Inexact Tensor Methods And Their Application To Stochastic Convex Optimization, Artem Agafonov, Dmitry Kamzolov, Pavel Dvurechensky, Alexander Gasnikov, Martin Takac
Machine Learning Faculty Publications
We propose general non-accelerated and accelerated tensor methods under inexact information on the derivatives of the objective, analyze their convergence rate. Further, we provide conditions for the inexactness in each derivative that is sufficient for each algorithm to achieve a desired accuracy. As a corollary, we propose stochastic tensor methods for convex optimization and obtain sufficient mini-batch sizes for each derivative. © 2020, CC BY.
Human Parsing Based Texture Transfer From Single Image To 3d Human Via Cross-View Consistency, Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao
Human Parsing Based Texture Transfer From Single Image To 3d Human Via Cross-View Consistency, Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao
Machine Learning Faculty Publications
This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic parsing of human body as input for providing both the shape and pose information to reduce the appearance variation of human image and preserve the spatial distribution of semantic parts. Meanwhile, in order to improve the prediction for textures of invisible parts, we explicitly enforce the consistency across different views of the same subject by exchanging the textures predicted by two views to render images during training. The perceptual loss …
Learning To Learn Kernels With Variational Random Features, Xiantong Zhen, Haoliang Sun, Yingjun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
Learning To Learn Kernels With Variational Random Features, Xiantong Zhen, Haoliang Sun, Yingjun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
Machine Learning Faculty Publications
We introduce kernels with random Fourier features in the meta-learning framework for few-shot learning. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta-learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTMbased inference network effectively integrates the context information of previous …