Learning Hard Alignments with Variational Inference

There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention can offer benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE and Q-learning to approach these issues, but those methods can provide high-variance gradient estimates and be slow to train. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced VIMCO and NVIL. Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task

Morphological and Biochemical Characterization of Different Oyster Mushroom (Pleurotus spp.)

Mushrooms have medicinal as well as nutritive value and extensively used as human food from the time immortal. In order to determine the genetic diversity among Pleurotus species of mushroom using morphological and random amplified polymorphic DNA (RAPD) markers, about seven different species were collected. Out of the seven, five species, naming Pleurotus citriopileatus, Pleurotus djamor, Pleurotus Florida, H. ulmarius and Pleurotus sajor-caju were selected. Five different morphological traits i.e., mycelial growth (mm), stipe length(cm), cap diameter (cm), margin of fruit body, colour of fruit body, total yield (kg), carbohydrate content(%) and protein content (%) were recorded. Results indicate that all the five species of Pleurotus shows great diversity in their morphological characters and biochemical parameters. Thus all these species have a great genetic diversity