Johns Hopkins University Applied Physics Laboratory

research

∙ 06/20/2022

Achieving Utility, Fairness, and Compactness via Tunable Information Bottleneck Measures

Designing machine learning algorithms that are accurate yet fair, not di...

Adam Gronowski, et al. ∙

research

∙ 01/01/2023

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

There are multiple scales of abstraction from which we can describe the ...

Jorge Quesada, et al. ∙

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

∙ 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

∙ 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

∙ 03/03/2022

Robustness and Adaptation to Hidden Factors of Variation

We tackle here a specific, still not widely addressed aspect, of AI robu...

William Paul, 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

∙ 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. ∙

research

∙ 01/31/2022

Network Programming via Computable Products

The User Plane Function (UPF) aims to provide network services in the 3G...

Dennis Volpano, et al. ∙

research

∙ 01/20/2022

Lifelong Learning Metrics

The DARPA Lifelong Learning Machines (L2M) program seeks to yield advanc...

Alexander New, et al. ∙

research

∙ 01/19/2022

Ordinal Maximin Share Approximation for Chores

We study the problem of fairly allocating a set of m indivisible chores ...

Hadi Hosseini, et al. ∙

research

∙ 05/02/2022

Triangular Dropout: Variable Network Width without Retraining

One of the most fundamental design choices in neural networks is layer w...

Edward W. Staley, 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

∙ 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

∙ 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

∙ 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

∙ 11/02/2021

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

∙ 03/09/2022

Renyi Fair Information Bottleneck for Image Classification

We develop a novel method for ensuring fairness in machine learning whic...

Adam Gronowski, 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

∙ 06/27/2022

Discrete Stochastic Optimization for Public Health Interventions with Constraints

Many public health threats exist, motivating the need to find optimal in...

Zewei Li, et al. ∙

research

∙ 07/27/2022

Structural Similarity for Improved Transfer in Reinforcement Learning

Transfer learning is an increasingly common approach for developing perf...

C. Chace Ashcraft, et al. ∙

research

∙ 07/28/2022

Latent Properties of Lifelong Learning Systems

Creating artificial intelligence (AI) systems capable of demonstrating l...

Corban Rivera, et al. ∙

research

∙ 08/02/2022

Curvature-informed multi-task learning for graph networks

Properties of interest for crystals and molecules, such as band gap, ela...

Alexander New, et al. ∙

research

∙ 08/02/2022

Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations

We propose an approach to solving partial differential equations (PDEs) ...

David Witman, et al. ∙

research

∙ 08/08/2022

Continual Reinforcement Learning with TELLA

Training reinforcement learning agents that continually learn across mul...

Neil Fendley, et al. ∙

research

∙ 08/12/2022

Autonomous Intelligent Software Development

We present an overview of the design and first proof-of-concept implemen...

Mark Alan Matties, et al. ∙

research

∙ 10/14/2022

Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations

In this work, we assess the ability of physics-informed neural networks ...

Alexander New, et al. ∙

research

∙ 10/14/2022

Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)

Approaches for stochastic nonlinear model predictive control (SNMPC) typ...

Adam Polevoy, et al. ∙

research

∙ 10/26/2022

Addressing Heterogeneity in Federated Learning via Distributional Transformation

Federated learning (FL) allows multiple clients to collaboratively train...

Haolin Yuan, et al. ∙

research

∙ 12/13/2022

Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems

We present a method for providing statistical guarantees on runtime safe...

Craig Knuth, et al. ∙

research

∙ 01/13/2023

Adaptive Neural Networks Using Residual Fitting

Current methods for estimating the required neural-network size for a gi...

Noah Ford, 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

∙ 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

∙ 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

∙ 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. ∙

Johns Hopkins University Applied Physics Laboratory

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