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Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly
Large language and vision models have been leading a revolution in visual computing. By greatly scaling up sizes of data and model parameters, the large models learn deep priors which lead to remarkable performance in various tasks.
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Such complexpatterns can be crucial when amodel is trained on MTS and might need ahuge amount of training samples to be captured by amachine learningalgorithm.
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Neural Information Processing Systems http://nips.cc/
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Maxim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alex Zhebrak
Neural Information Processing Systems http://nips.cc/
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Estimating the generalization performance is practically challenging on out-of-distribution (OOD) data without ground-truth labels.
Superposition of many models into one
Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno Olshausen
Neural Information Processing Systems http://nips.cc/
Contour location via entropy reduction leveraging multiple information sources
Alexandre Marques, Remi Lam, Karen Willcox
Neural Information Processing Systems http://nips.cc/
