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Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Ensemble approaches for uncertainty estimation have recently been applied to the tasks of misclassification detection, out-of-distribution input detection and adversarial attack detection. Prior Networks have been proposed as an approach to efficiently emulate an ensemble of models for classification by parameteris-ing a Dirichlet prior distribution over output distributions.
Can Language Models Solve Graph Problems in Natural Language?
Graph), a comprehensive benchmark of graph-based problem solving designed in natural language.
Backpropagation-Friendly Eigendecomposition
Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann
Neural Information Processing Systems http://nips.cc/
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices
Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, Wonyong Sung
Neural Information Processing Systems http://nips.cc/
ed189de2611f200bd4c2ab30c576e99e-Supplemental-Conference.pdf
Because the above experiments are based on a VGG source model condition, we try to find out whether SACfingerprints thesource model successfully ondifferentmodel architectures.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling
Neural Information Processing Systems http://nips.cc/
Direct Estimation of Differential Functional Graphical Models
Boxin Zhao, Y. Samuel Wang, Mladen Kolar
Neural Information Processing Systems http://nips.cc/
Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
Besides evaluating the next-item prediction, we estimate the models' capabilities to capture