A Hybrid Convolutional Variational Autoencoder for Text Generation

In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components with a recurrent language model. Our architecture exhibits several attractive properties such as faster run time and convergence, ability to better handle long sequences and, more importantly, it helps to avoid some of the major difficulties posed by training VAE models on textual data

Discovering candidates for gene network expansion by distributed volunteer computing

Our group has recently developed gene@home, a BOINC project that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The gene@home project adopts intensive variable-subsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC client. Our project exploits the PC algorithm (Spirtes and Glymour, 1991) in an iterative way, for discovering putative causal relationships within each subset of variables. This paper presents our infrastructure, called TN-Grid, that is hosting the gene@home project. Gene@home implements a novel method for Network Expansion by Subsetting and Ranking Aggregation (NESRA), producing a list of genes that are candidates for the gene network expansion task. NESRA is an algorithm that has: 1) a ranking procedure that systematically subsets the variables; the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found; 2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists; 3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. In our experimental results, we show that NESRA outperforms both the PC algorithm and its order-independent version called PC*. Evaluations and experiments are done by means of the gene@home project on a real gene regulatory network of the model plant Arabidopsis thaliana