Search Results for author: Ifeoma Nwogu

Found 7 papers, 0 papers with code

WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments

no code implementations1 Nov 2020 Tharindu Ranasinghe, Sarthak Gupte, Marcos Zampieri, Ifeoma Nwogu

This paper describes the WLV-RIT entry to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) shared task 2020.

Language Identification Transfer Learning +1

Modeling Global Body Configurations in American Sign Language

no code implementations3 Sep 2020 Nicholas Wilkins, Beck Cordes Galbraith, Ifeoma Nwogu

Finally, when compared with spoken languages, such as English, there has been limited research conducted into the linguistics of ASL.

Machine Translation

Regression with Uncertainty Quantification in Large Scale Complex Data

no code implementations4 Dec 2019 Nicholas Wilkins, Michael Johnson, Ifeoma Nwogu

While several methods for predicting uncertainty on deep networks have been recently proposed, they do not readily translate to large and complex datasets.

Age Estimation Time Series

Dimensionality Reduction with Subspace Structure Preservation

no code implementations NeurIPS 2014 Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju

Modeling data as being sampled from a union of independent subspaces has been widely applied to a number of real world applications.

Dimensionality Reduction

A Concept Learning Approach to Multisensory Object Perception

no code implementations23 Sep 2014 Ifeoma Nwogu, Goker Erdogan, Ilker Yildirim, Robert Jacobs

This paper presents a computational model of concept learning using Bayesian inference for a grammatically structured hypothesis space, and test the model on multisensory (visual and haptics) recognition of 3D objects.

Bayesian Inference

Is Joint Training Better for Deep Auto-Encoders?

no code implementations6 May 2014 Yingbo Zhou, Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju

But due to the greedy scheme of the layerwise training technique, the parameters of lower layers are fixed when training higher layers.

An Analysis of Random Projections in Cancelable Biometrics

no code implementations17 Jan 2014 Devansh Arpit, Ifeoma Nwogu, Gaurav Srivastava, Venu Govindaraju

With increasing concerns about security, the need for highly secure physical biometrics-based authentication systems utilizing \emph{cancelable biometric} technologies is on the rise.

Face Recognition

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