Papers Unity

A Learning Theory in Linear Systems under Compositional Models

We present a learning theory for the training of a linear system operator having an input compositional variable and propose a Bayesian inversion method for inferring the unknown variable from an output of a noisy linear…

Feature-less Stitching of Cylindrical Tunnel

Traditional image stitching algorithms use transforms such as homography to combine different views of a scene. They usually work well when the scene is planar or when the camera is only rotated, keeping its position sta…

VLSI Design Of Advanced Digital Filters

The Cascaded Integrator Comb filters (CIC) find many applications in recent electronic devices such as frequency selection functions in a digital radio or modem and any filter structure that is required to efficiently pr…

The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic

Although simple individually, artificial neurons provide state-of-the-art performance when interconnected in deep networks. Unknown to many, there exists an arguably even simpler and more versatile learning mechanism, na…

Batch Normalization and the impact of batch structure on the behavior of deep convolution networks

Batch normalization was introduced in 2015 to speed up training of deep convolution networks by normalizing the activations across the current batch to have zero mean and unity variance. The results presented here show a…

Small Signal Audiosusceptibility Model for Series Resonant Converter

Models that accurately predict the output voltage ripple magnitude are essential for applications with stringent performance target for it. Impact of dc input ripple on the output ripple for a Series Resonant Converter (…

An Integrated Soft Computing Approach to a Multi-biometric Security Model

The abstract of the thesis consists of three sections, videlicet, Motivation Chapter Organization Salient Contributions. The complete abstract is included with the thesis. The final section on Salient Contributio…

Analysis of a model for hepatitis C virus transmission that includes the effects of vaccination with waning immunity

This paper considers a mathematical model based on the transmission dynamics of hepatitis C virus (HCV) infection. In addition to the usual compartments for susceptible, exposed, and infected individuals, this model incl…

Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars

As an initial assessment, over 480,000 labeled virtual images of normal highway driving were readily generated in Grand Theft Auto V's virtual environment. Using these images, a CNN was trained to detect following distan…

PRUNE: Preserving Proximity and Global Ranking for Network Embedding

We investigate an unsupervised generative approach for network embedding. A multi-task Siamese neural network structure is formulated to connect embedding vectors and our objective to preserve the global node ranking and…

Online control of the false discovery rate with decaying memory

In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypothesis must be made immediately, after whi…

Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages

KillingFusion: Non-Rigid 3D Reconstruction Without Correspondences

We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors. To this end, we tackle the problem of non-rigid registration…

Building Multimodal Simulations for Natural Language

In this tutorial, we introduce a computational framework and modeling language (VoxML) for composing multimodal simulations of natural language expressions within a 3D simulation environment (VoxSim). We demonstrate how …

Data driven partition-of-unity copulas with applications to risk management

We present a constructive and self-contained approach to data driven general partition-of-unity copulas that were recently introduced in the literature. In particular, we consider Bernstein-, negative binomial and Poisso…

On SGD's Failure in Practice: Characterizing and Overcoming Stalling

Stochastic Gradient Descent (SGD) is widely used in machine learning problems to efficiently perform empirical risk minimization, yet, in practice, SGD is known to stall before reaching the actual minimizer of the empiri…

An unexpected unity among methods for interpreting model predictions

Understanding why a model made a certain prediction is crucial in many data science fields. Interpretable predictions engender appropriate trust and provide insight into how the model may be improved. However, with large…

Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging

We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconst…

Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors

Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivativ…

Generating Simulations of Motion Events from Verbal Descriptions

In this paper, we describe a computational model for motion events in natural language that maps from linguistic expressions, through a dynamic event interpretation, into three-dimensional temporal simulations in a model…