Latent Variable Models (LVMs) are a family of machine learning (ML) models that have been widely used in text mining, computer vision, computational biology, recommender system, to name a few. One central task in machine learning is to extract the latent knowledge and structure from observed data and LVMs elegantly fit into this task. LVMs consist of observed variables used for modeling observe...

Noisy, high-dimensional time series observations can often be described by a set of low-dimensional latent variables. Commonly used methods to extract these latent variables typically assume instantaneous relationships between the latent and observed variables. In many physical systems, changes in the latent variables manifest as changes in the observed variables after time delays. Techniques t...

Autoregressive sequence models based on deep neural networks, such as RNNs, Wavenet and the Transformer attain state-of-the-art results on many tasks. However, they are difficult to parallelize and are thus slow at processing long sequences. RNNs lack parallelism both during training and decoding, while architectures like WaveNet and Transformer are much more parallelizable during training, yet...

Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that run...

Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation* This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a statespace representation of the second-order solution based on the ‘pruning’ ...

This paper investigates the possibility of extracting latent aspects of a video in order to develop a video fingerprinting framework. Semantic visual information about humans, more specifically face occurrences in video frames, along with a generative probabilistic model, namely the Latent Dirichlet Allocation (LDA), are utilized for this purpose. The latent variables, namely the video topics a...

The presence of latent variables can greatly complicate inferences about causal relations between measured variables from statistical data. In many cases, the presence of latent variables makes it impossible to determine for two measured variables A and B, whether A causes B, B causes A, or there is some common cause. In this paper I present several theorems that state conditions under which it...

Structural equation modeling originated (Jöreskog (1973), Bentler (1980), Bollen (1989)) as a method for modeling linear relations among observed and hypothesized latent variables. Despite limitation inherent in the linearity assumption of traditional structural equation modeling, it has indeed provided a revolutionary and popular framework for addressing research questions in the social, psych...

The discovery of latent information in large-scale databases has become a major problem of information technology and statistics in the recent years. The investigation of relational data, e.g. the structures of graphs is an important field of this problem. One notable task is the clustering of the nodes of the graph. This means the discovery of cohesive groups and the modelling of the cohesion....

Insight plays an important role in the management of Schizophrenia. The study was undertaken to assess the cross-sectional relationship of clinical variables and psychopathology to insight, using the BPRS and the Insight Schedule. The study sample consisted of 59ICD-10 Schizophrenics with a mean duration of illness of 41.88 months. Insight was found to have significant positive association with...