Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in largescale video annotation due to several difficulties, such as large variation of video conte...

A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make parametric assumptions about the form of the encoder distribution. Performance is further enhanced by integrating the proposed encoder with importance sampling. Excellent performance is demonstrated across multiple unsupe...

We describe the use of the semi-empirical molecular orbital method PM3 (parametric method 3) to study the electronic structure of iron-sulphur proteins. We first develop appropriate parameters to describe models of the redox site of rubredoxins, followed by some preliminary calculations of multinuclear iron systems of relevance to hydrogenases.

Current methods used to analyze time to event data either rely on highly parametric assumptions which result in biased estimates of parameters which are purely chosen out of convenience, or are highly unstable because they ignore the global constraints of the true model. By using Targeted Maximum Likelihood Estimation (TMLE) one may consistently estimate parameters which directly answer the sta...

A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make parametric assumptions about the form of the encoder distribution. Performance is further enhanced by integrating the proposed encoder with importance sampling. Excellent performance is demonstrated across multiple unsupe...

Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many data mining applications in recent years. This paper addresses the semi-supervised learning problem by developing a semiparametric regularization based approach, which attempts to discover the marginal distribution of...

Let Q be the field of rational numbers and Q[u1, ..., ud, x1, ..., xs] the ring of polynomials in n indeterminates with coefficients in Q and d + s = n (0 ≤ d < n). The indeterminates are divided into two groups: u = (u1, ..., ud) and x = (x1, ..., xs), which are called parameters and variables, respectively. A polynomial set is a finite set of nonzero polynomials in Q[u,x]. The following syste...

Recent advances in the econometric modelling of count data have often been based on the generalized method of moments (GMM). However, the two-step GMM procedure may perform poorly in small samples, and several empirical likelihood-based estimators have been suggested alternatively. In this paper I discuss empirical likelihood (EL) estimation for count data models with endogenous regressors. I c...