Johns Hopkins University Applied Physics Laboratory

research

∙ 07/28/2021

Generalizing Fairness: Discovery and Mitigation of Unknown Sensitive Attributes

Ensuring trusted artificial intelligence (AI) in the real world is an cr...

William Paul, et al. ∙

research

∙ 02/25/2020

Unsupervised Semantic Attribute Discovery and Control in Generative Models

This work focuses on the ability to control via latent space factors sem...

William Paul, et al. ∙

research

∙ 09/13/2021

Shape-Biased Domain Generalization via Shock Graph Embeddings

There is an emerging sense that the vulnerability of Image Convolutional...

Maruthi Narayanan, et al. ∙

research

∙ 03/16/2021

Towards Indirect Top-Down Road Transport Emissions Estimation

Road transportation is one of the largest sectors of greenhouse gas (GHG...

Ryan Mukherjee, et al. ∙

research

∙ 11/20/2018

Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery

Semantic segmentation is a powerful method to facilitate visual scene un...

Marc Bosch, et al. ∙

research

∙ 03/22/2019

Pose Estimation of Periacetabular Osteotomy Fragments with Intraoperative X-Ray Navigation

Objective: State of the art navigation systems for pelvic osteotomies us...

Robert B. Grupp, et al. ∙

research

∙ 11/18/2021

Complex Terrain Navigation via Model Error Prediction

Robot navigation traditionally relies on building an explicit map that i...

Adam Polevoy, et al. ∙

research

∙ 11/28/2017

Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning

Recent advances in Generative Adversarial Learning allow for new modalit...

Marc Bosch, et al. ∙

research

∙ 03/04/2015

Statistical modality tagging from rule-based annotations and crowdsourcing

We explore training an automatic modality tagger. Modality is the attitu...

Vinodkumar Prabhakaran, et al. ∙

research

∙ 02/05/2015

Use of Modality and Negation in Semantically-Informed Syntactic MT

This paper describes the resource- and system-building efforts of an eig...

Kathryn Baker, et al. ∙

research

∙ 09/24/2014

Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach

We describe a unified and coherent syntactic framework for supporting a ...

Kathryn Baker, et al. ∙

research

∙ 03/23/2016

Evaluating semantic models with word-sentence relatedness

Semantic textual similarity (STS) systems are designed to encode and eva...

Kimberly Glasgow, et al. ∙

research

∙ 01/22/2018

On The Effect of Vibration on Shape Sensing of Continuum Manipulators Using Fiber Bragg Gratings

Fiber Bragg Grating (FBG) has shown great potential in shape and force s...

Shahriar Sefati, et al. ∙

research

∙ 01/21/2018

A Convex Optimization Framework for Constrained Concurrent Motion Control of a Hybrid Redundant Surgical System

We present a constrained motion control framework for a redundant surgic...

Farshid Alambeigi, et al. ∙

research

∙ 01/22/2018

Communication Model-Task Pairing in Artificial Swarm Design

Unraveling the nature of the communication model that governs which two ...

Musad Haque, et al. ∙

research

∙ 03/06/2018

Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation

Fast, collision-free motion through unknown environments remains a chall...

Kapil Katyal, et al. ∙

research

∙ 07/01/2018

FBG-Based Control of a Continuum Manipulator Interacting With Obstacles

Tracking and controlling the shape of continuum dexterous manipulators (...

Shahriar Sefati, et al. ∙

research

∙ 11/04/2018

Modeling Traffic Networks Using Integrated Route and Link Data

Real-time navigation services, such as Google Maps and Waze, are widely ...

Xilei Zhao, et al. ∙

research

∙ 11/07/2018

On the Complexity of Reconnaissance Blind Chess

This paper provides a complexity analysis for the game of reconnaissance...

Jared Markowitz, et al. ∙

research

∙ 12/20/2018

FBG-Based Position Estimation of Highly Deformable Continuum Manipulators: Model-Dependent vs. Data-Driven Approaches

Conventional shape sensing techniques using Fiber Bragg Grating (FBG) in...

Shahriar Sefati, et al. ∙

research

∙ 03/26/2019

Error Analysis for the Particle Filter: Methods and Theoretical Support

The particle filter is a popular Bayesian filtering algorithm for use in...

Ziyu Liu, et al. ∙

research

∙ 04/09/2019

Malicious Overtones: hunting data theft in the frequency domain with one-class learning

A method for detecting electronic data theft from computer networks is d...

Brian A. Powell, et al. ∙

research

∙ 06/04/2019

Closed-Loop Control of a Delta-Wing Unmanned Aerial-Aquatic Vehicle

We present a closed-loop control strategy for a delta-wing unmanned aeri...

Joseph Moore, et al. ∙

research

∙ 06/23/2019

Efficient Implementation of Second-Order Stochastic Approximation Algorithms in High-Dimensional Problems

Stochastic approximation (SA) algorithms have been widely applied in min...

Jingyi Zhu, et al. ∙

research

∙ 08/12/2019

Learning to Detect Collisions for Continuum Manipulators without a Prior Model

Due to their flexibility, dexterity, and compact size, Continuum Manipul...

Shahriar Sefati, et al. ∙

research

∙ 02/25/2020

TanksWorld: A Multi-Agent Environment for AI Safety Research

The ability to create artificial intelligence (AI) capable of performing...

Corban G. Rivera, et al. ∙

research

∙ 05/01/2020

Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks

We focus on the development of effective adversarial patch attacks and –...

Neil Fendley, et al. ∙

research

∙ 06/22/2020

PICO: Primitive Imitation for COntrol

In this work, we explore a novel framework for control of complex system...

Corban G. Rivera, et al. ∙

research

∙ 04/06/2021

Speaker Diarization using Two-pass Leave-One-Out Gaussian PLDA Clustering of DNN Embeddings

Many modern systems for speaker diarization, such as the recently-develo...

Kiran Karra, et al. ∙

research

∙ 08/05/2021

Role-based lateral movement detection with unsupervised learning

Adversarial lateral movement via compromised accounts remains difficult ...

Brian A. Powell, et al. ∙

research

∙ 08/13/2021

Random Subspace Mixture Models for Interpretable Anomaly Detection

We present a new subspace-based method to construct probabilistic models...

Cetin Savkli, et al. ∙

research

∙ 08/16/2021

Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?

Perturbation-based attacks, while not physically realizable, have been t...

Max Lennon, et al. ∙

research

∙ 09/09/2021

SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks

The application of self-supervised methods has resulted in broad improve...

Kiran Karra, et al. ∙

research

∙ 09/04/2021

Ordinal Maximin Share Approximation for Goods

In fair division of indivisible goods, l-out-of-d maximin share (MMS) is...

Hadi Hosseini, et al. ∙

research

∙ 11/01/2021

Machine Learning aided Crop Yield Optimization

We present a crop simulation environment with an OpenAI Gym interface, a...

Chace Ashcraft, et al. ∙

research

∙ 11/02/2021

Instructive artificial intelligence (AI) for human training, assistance, and explainability

We propose a novel approach to explainable AI (XAI) based on the concept...

Nicholas Kantack, et al. ∙

research

∙ 11/18/2021

Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates

In 2021 the Johns Hopkins University Applied Physics Laboratory held an ...

Nicholas Kantack, et al. ∙

research

∙ 11/18/2021

Visual Goal-Directed Meta-Learning with Contextual Planning Networks

The goal of meta-learning is to generalize to new tasks and goals as qui...

Corban G. Rivera, et al. ∙

research

∙ 12/14/2021

Scatterbrained: A flexible and expandable pattern for decentralized machine learning

Federated machine learning is a technique for training a model across mu...

Miller Wilt, et al. ∙

research

∙ 01/04/2022

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) t...

Adam Polevoy, et al. ∙

research

∙ 01/13/2022

How I learned to stop worrying and love the curse of dimensionality: an appraisal of cluster validation in high-dimensional spaces

The failure of the Euclidean norm to reliably distinguish between nearby...

Brian A. Powell, et al. ∙

Johns Hopkins University Applied Physics Laboratory

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