Accuracy
Precision-Recall Balanced Topic Modelling
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Stacked Capsule Autoencoders
Adam Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton
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9 Appendix Supplementary material for the paper Causal analysis of 19 spread in Germany
W in V, W is independent of V\ ( Descendants(W) Parents( W)) given Parents (W) . As expected we see that the number of detected causes by Granger is multiple times more than those of SyPI; in most cases Granger detects as causes all the candidate states. On the other hand, SyPI does not suffer from such problems even when there are latent confounders. Finally, in the third column, we report the detected distant causes. Strict thresholds (the default of SyPI method) are used for the analysis.
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization Haoliang Li1Y uFei Wang 1 Renjie Wan 1 Shiqi Wang 2
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible in clinically realistic environments.
Learning structured densities via infinite dimensional exponential families
Siqi Sun, Mladen Kolar, Jinbo Xu
Learning the structure of a probabilistic graphical models is a well studied problem in the machine learning community due to its importance in many applications. Current approaches are mainly focused on learning the structure under restrictive parametric assumptions, which limits the applicability of these methods. In this paper, we study the problem of estimating the structure of a probabilistic graphical model without assuming a particular parametric model. We consider probabilities that are members of an infinite dimensional exponential family [4], which is parametrized by a reproducing kernel Hilbert space (RKHS) H and its kernel k . One difficulty in learning nonparametric densities is the evaluation of the normalizing constant. In order to avoid this issue, our procedure minimizes the penalized score matching objective [10, 11]. We show how to efficiently minimize the proposed objective using existing group lasso solvers. Furthermore, we prove that our procedure recovers the graph structure with high-probability under mild conditions. Simulation studies illustrate ability of our procedure to recover the true graph structure without the knowledge of the data generating process.
Intra-Processing Methods for Debiasing Neural Networks
However, in computer vision or natural language applications, it is common to start with a large generic model and then fine-tune to a specific use-case.
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data.
Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, Balaji Lakshminarayanan
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Consistent Feature Selection for Analytic Deep Neural Networks
One of the most important steps toward model interpretability is feature selection, which aims to identify the subset of relevant features with respect to an outcome.