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In this thesis, we present a transformers-based multi-lingual embedding model to represent sentences in different languages in a common space. To do so, our system uses the structure of a simplified transformer with a shared byte-pair encoding vocabulary f ...
Comic book digitization would play a pivotal role in exploring new avenues on how digital comics can be consumed. As of present, the systems capable of doing such a task are limited in capability to achieve complete digitization. This task of digitization ...
We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations ...
Convolutional neural networks (CNNs) based approaches for semantic alignment and object landmark detection have improved their performance significantly. Current efforts for the two tasks focus on addressing the lack of massive training data through weakly ...
The problem of finding a k×k submatrix of maximum volume of a matrix A is of interest in a variety of applications. For example, it yields a quasi-best low-rank approximation constructed from the rows and columns of A. We show that such a submatrix ...
There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding doc ...
We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation a ...
Many interesting applications emerged with the increasing popularity of deep learning. This project explored natural language processing and visualization techniques as well as two neural network architectures to classify ICOs. The first network focused on ...
Towards the goal of improving acoustic modeling for automatic speech recognition (ASR), this work investigates the modeling of senone subspaces in deep neural network (DNN) posteriors using low-rank and sparse modeling approaches. While DNN posteriors are ...
In Time-Sensitive Networking (TSN), it is important to formally prove per-flow latency and backlog bounds. To this end, recent works have applied network calculus and obtained latency bounds from service curves. The latency component of such service curves ...