Deep Independent Component Estimation (DICE) has many applications in modern
day machine learning as a feature engineering extraction method. We provide a
novel latent variable representation of independent component analysis that
enables both point estimates via expectation-maximization (EM) and full
posterior sampling via Markov Chain Monte Carlo (MCMC) algorithms. Our
methodology also applies to flow-based methods for nonlinear feature
extraction. We discuss how to implement conditional posteriors and
envelope-based methods for optimization. Through this representation hierarchy,
we unify a number of hitherto disjoint estimation procedures. We illustrate our
methodology and algorithms on a numerical example. Finally, we conclude with
directions for future research.

深度独立分量估计（DICE）是一种现代机器学习中用于特征工程提取的方法，通过期望最大化（EM）和马尔可夫链蒙特卡罗（MCMC）算法，我们提供了独立分量分析的新型潜变量表示，该方法还适用于非线性特征提取的流体方法，并讨论了如何实现条件后验和基于包络的优化方法，通过这种表示层次，我们统一了许多迄今为止不相关的估计过程，在数值实例上说明了我们的方法和算法，最后，我们总结了未来研究方向。

贝叶斯深度冰

Bayesian Deep ICE

In this paper, we present the Inter-Battery Topic Model (IBTM). Our approach
extends traditional topic models by learning a factorized latent variable
representation. The structured representation leads to a model that marries
benefits traditionally associated with a discriminative approach, such as
feature selection, with those of a generative model, such as principled
regularization and ability to handle missing data. The factorization is
provided by representing data in terms of aligned pairs of observations as
different views. This provides means for selecting a representation that
separately models topics that exist in both views from the topics that are
unique to a single view. This structured consolidation allows for efficient and
robust inference and provides a compact and efficient representation. Learning
is performed in a Bayesian fashion by maximizing a rigorous bound on the
log-likelihood. Firstly, we illustrate the benefits of the model on a synthetic
dataset,. The model is then evaluated in both uni- and multi-modality settings
on two different classification tasks with off-the-shelf convolutional neural
network (CNN) features which generate state-of-the-art results with extremely
compact representations.

本文介绍了 Inter-Battery Topic Model（IBTM）及其基于因式分解的潜变量表示方法，结合了特征选择和正则化等优势，并提供了一种用于单一视角和两个视角数据上的特征抽取与分类的方法。

跨电池主题表示学习

Inter-Battery Topic Representation Learning

A mean field variational Bayes approach to support vector machines (SVMs)
using the latent variable representation on Polson & Scott (2012) is presented.
This representation allows circumvention of many of the shortcomings associated
with classical SVMs including automatic penalty parameter selection, the
ability to handle dependent samples, missing data and variable selection. We
demonstrate on simulated and real datasets that our approach is easily
extendable to non-standard situations and outperforms the classical SVM
approach whilst remaining computationally efficient.

使用潜在变量表示的均值场变分贝叶斯方法应用于支持向量机，能克服传统 SVM 的许多缺点，包括自动惩罚参数选择，处理相关样本、缺失数据和变量选择的能力，并在模拟和真实数据集上展示了我们的方法在非传统情况下的可扩展性和计算效率高于传统的 SVM 方法。