Better Word Embeddings by Disentangling Contextual n-Gram Information

Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word embeddings along with higher n-gram embeddings helps in the removal of the contextual information from the unigrams, resulting in better stand-alone word embeddings. We empirically show the validity of our hypothesis by outperforming other competing word representation models by a significant margin on a wide variety of tasks. We make our models publicly available.Comment: NAACL 201

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.Comment: NAACL 201

A complete metric topology on relative low energy spaces

In this paper, we show that the low energy spaces in the prescribed singularity case $\mathcal{E}_{\psi}(X,\theta,\phi)$ have a natural topology which is completely metrizable. This topology is stronger than convergence in capacity.Comment: 25 pages, comments are welcom

Saliency Prediction for Mobile User Interfaces

We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons, text, etc. in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied area. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. We first collected eye-gaze data from mobile devices for free viewing task. Using this data, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.Comment: Paper accepted at WACV 201

Finite Element Static Analysis of Slabs on Elastic Foundation

The Finite Element Method (FEM) is a numerical technique for finding approximate solutions to boundary value problems for partial differential equations. It uses subdivision of whole problem domain into simpler parts, called finite elements, and variational methods from the calculus of variations to solve the problem by minimizing the associated error function. Analogous to the idea that connecting many tiny straight lines can approximate a larger circle, FEM encompasses methods for connecting many simple element equations over many small subdomains, named finite elements, to approximate a more complex equation over a larger domain. Concrete building slabs (plates), upheld directly by the soil medium, is a common construction form. It is utilized as a part of private, business, mechanical, and institutional structures. In some of these structures, substantial slab loads occur, for example, in libraries, grain stockpiling structures, distribution centres, and so forth. A mat foundation, which is usually utilized as a part of the supporting of multi-story building sections, is another illustration of a vigorously loaded concrete slab supported directly by the soil medium. In every one of these structures, it is vital to compute slab displacements and consequent stresses with a worthy level of precision so as to guarantee a sheltered and practical configuration. This project presents a finite element static analysis for estimating the structural behaviour of plates resting on elastic foundations, described by the Winkler’s Model. A Matlab program computing the displacement and stresses for slabs on elastic foundation has been presented in the appendix