Search Results for author: Tim Oates

Found 31 papers, 5 papers with code

Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

no code implementations30 Sep 2021 Sourav Mukherjee, David Widmark, Vince DiMascio, Tim Oates

Accurate specification of standard occupational classification (SOC) code is critical to the success of many U. S. work visa applications.

Learning with Holographic Reduced Representations

no code implementations5 Sep 2021 Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors \cite{Plate1995} by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects.

Multi-Label Classification

Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment

no code implementations30 Jun 2021 Ashwinkumar Ganesan, Francis Ferraro, Tim Oates

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective.


Semiring Primitives for Sparse Neighborhood Methods on the GPU

1 code implementation13 Apr 2021 Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, John Zedlewski, Tim Oates

High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typically not issues in dense operations.

Information Retrieval

Immigration Document Classification and Automated Response Generation

no code implementations29 Sep 2020 Sourav Mukherjee, Tim Oates, Vince DiMascio, Huguens Jean, Rob Ares, David Widmark, Jaclyn Harder

In this paper, we consider the problem of organizing supporting documents vital to U. S. work visa petitions, as well as responding to Requests For Evidence (RFE) issued by the U. S.~Citizenship and Immigration Services (USCIS).

Classification Document Classification +1

Bringing UMAP Closer to the Speed of Light with GPU Acceleration

1 code implementation1 Aug 2020 Corey J. Nolet, Victor Lafargue, Edward Raff, Thejaswi Nanditale, Tim Oates, John Zedlewski, Joshua Patterson

The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning.

Locality Preserving Loss: Neighbors that Live together, Align together

no code implementations7 Apr 2020 Ashwinkumar Ganesan, Francis Ferraro, Tim Oates

We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations.

Natural Language Inference Sentence Embeddings +1

Using Neural Networks for Programming by Demonstration

no code implementations10 Oct 2019 Karan K. Budhraja, Hang Gao, Tim Oates

A low time-complexity and data requirement favoring framework for reproducing emergent behavior, given an abstract demonstration, is discussed in [1], [2].

Universal Adversarial Perturbation for Text Classification

no code implementations10 Oct 2019 Hang Gao, Tim Oates

Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability.

Adversarial Text Classification +2

Learning from Observations Using a Single Video Demonstration and Human Feedback

no code implementations29 Sep 2019 Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas Waytowich

In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the visual representation of the demonstration.

Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter

no code implementations12 Sep 2019 Chi Zhang, Bryan Wilkinson, Ashwinkumar Ganesan, Tim Oates

Another way to remove that limitation, an optional classification layer, trained on manually annotated DoS attack tweets, to filter out non-attack tweets can be used to increase precision at the expense of recall.

Graph Node Embeddings using Domain-Aware Biased Random Walks

no code implementations8 Aug 2019 Sourav Mukherjee, Tim Oates, Ryan Wright

In this paper, we demonstrate that semantic information can play a useful role in computing graph embeddings.

Graph Embedding

Hybrid Mortality Prediction using Multiple Source Systems

no code implementations18 Apr 2019 Isaac Mativo, Yelena Yesha, Michael Grasso, Tim Oates, Qian Zhu

The use of artificial intelligence in clinical care to improve decision support systems is increasing.

Mortality Prediction

Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning

no code implementations28 Mar 2019 Ashwinkumar Ganesan, Pooja Parameshwarappa, Akshay Peshave, ZhiYuan Chen, Tim Oates

In this paper, we proposeaprobabilistic abductive reasoningapproach that augments an exist-ing rule-based IDS (snort [29]) to detect these evolved attacks by (a)Predicting rule conditions that are likely to occur (based on existingrules) and (b) able to generate new snort rules when provided withseed rule (i. e. a starting rule) to reduce the burden on experts toconstantly update them.

Intrusion Detection

On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning

no code implementations25 Mar 2019 Bharat Prakash, Mark Horton, {Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin

This compression model is vital to efficiently learn policies, especially when learning on embedded systems.

Automated Cloud Provisioning on AWS using Deep Reinforcement Learning

1 code implementation13 Sep 2017 Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi

As the use of cloud computing continues to rise, controlling cost becomes increasingly important.

Q-Learning Transfer Learning

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

no code implementations28 Aug 2017 JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors.

EEG General Classification +1

Fashioning with Networks: Neural Style Transfer to Design Clothes

no code implementations31 Jul 2017 Prutha Date, Ashwinkumar Ganesan, Tim Oates

Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis.

Object Detection Object Recognition +3

Identifying Spatial Relations in Images using Convolutional Neural Networks

no code implementations13 Jun 2017 Mandar Haldekar, Ashwinkumar Ganesan, Tim Oates

Traditional approaches to building a large scale knowledge graph have usually relied on extracting information (entities, their properties, and relations between them) from unstructured text (e. g. Dbpedia).

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

6 code implementations20 Nov 2016 Zhiguang Wang, Weizhong Yan, Tim Oates

We propose a simple but strong baseline for time series classification from scratch with deep neural networks.

General Classification Time Series +1

Neuroevolution-Based Inverse Reinforcement Learning

no code implementations9 Aug 2016 Karan K. Budhraja, Tim Oates

One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards.

Adopting Robustness and Optimality in Fitting and Learning

no code implementations13 Oct 2015 Zhiguang Wang, Tim Oates, James Lo

We generalized a modified exponentialized estimator by pushing the robust-optimal (RO) index $\lambda$ to $-\infty$ for achieving robustness to outliers by optimizing a quasi-Minimin function.

Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks

no code implementations24 Sep 2015 Zhiguang Wang, Tim Oates

We propose an off-line approach to explicitly encode temporal patterns spatially as different types of images, namely, Gramian Angular Fields and Markov Transition Fields.

Classification General Classification +1

Adaptive Normalized Risk-Averting Training For Deep Neural Networks

no code implementations8 Jun 2015 Zhiguang Wang, Tim Oates, James Lo

This paper proposes a set of new error criteria and learning approaches, Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex optimization problem in training deep neural networks (DNNs).

Imaging Time-Series to Improve Classification and Imputation

3 code implementations1 Jun 2015 Zhiguang Wang, Tim Oates

We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images.

Classification General Classification +3

Detecting Epileptic Seizures from EEG Data using Neural Networks

no code implementations19 Dec 2014 Siddharth Pramod, Adam Page, Tinoosh Mohsenin, Tim Oates

We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG).


Cannot find the paper you are looking for? You can Submit a new open access paper.