Directed Networks
Model evidence from nonequilibrium simulations
The marginal likelihood, or model evidence, is a key quantity in Bayesian parameter estimation and model comparison.
Robust Conditional Probabilities
Conditional probabilities are a core concept in machine learning.
Independence clustering (without a matrix)
Since mutual independence is the target, pairwise similarity measurements are of no use, and thus traditional clustering algorithms are inapplicable.
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, xuejun Liao, Lawrence Carin
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions.
Flexible Models for Microclustering with Application to Entity Resolution
Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna Wallach, Abbas Zaidi, Beka Steorts
However, for some applications, this assumption is inappropriate.
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Alex M. Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
GibbsNet is the best of both worlds both in theory and in practice.
Deep Dynamic Poisson Factorization Model
A new model, named as deep dynamic poisson factorization model, is proposed in this paper for analyzing sequential count vectors.
Permutation-based Causal Inference Algorithms with Interventions
Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler
In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption.
Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov, Stefano Ermon
Our approach offers a number of advantages over previous methods.
Introspective Classification with Convolutional Nets
Long Jin, Justin Lazarow, Zhuowen Tu
We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory.