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26 papers · 1 filter
Spatio-Temporal Variational Gaussian Processes
Oliver Hamelijnck, William J. Wilkinson, Niki A. Loppi +2
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP…
NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning
Haoxing Ren, Matthew Fojtik, Brucek Khailany
High quality standard cell layout automation in advanced technology nodes is still challenging in the industry today because of complex design rules. In this paper we introduce an…
Identifying Layers Susceptible to Adversarial Attacks
Shoaib Ahmed Siddiqui, Thomas Breuel
In this paper, we investigate the use of pretraining with adversarial networks, with the objective of discovering the relationship between network depth and robustness. For this pu…
On Fast Sampling of Diffusion Probabilistic Models
Zhifeng Kong, Wei Ping
In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models. FastDPM generalizes previous methods and gives rise to new algorithms wit…
f-Domain-Adversarial Learning: Theory and Algorithms
David Acuna, Guojun Zhang, Marc T. Law +1
Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labele…
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework
Jiahao Su, Wonmin Byeon, Furong Huang
Enforcing orthogonality in neural networks is an antidote for gradient vanishing/exploding problems, sensitivity by adversarial perturbation, and bounding generalization errors. Ho…