Search Results for author: Chace Ashcraft

Found 11 papers, 2 papers with code

A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)

no code implementations16 Feb 2023 Jennifer Sleeman, David Chung, Anand Gnanadesikan, Jay Brett, Yannis Kevrekidis, Marisa Hughes, Thomas Haine, Marie-Aude Pradal, Renske Gelderloos, Chace Ashcraft, Caroline Tang, Anshu Saksena, Larry White

We describe an adversarial game to explore the parameter space of these models, detect upcoming tipping points, and discover the drivers of tipping points.

Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points

no code implementations14 Feb 2023 Jennifer Sleeman, David Chung, Chace Ashcraft, Jay Brett, Anand Gnanadesikan, Yannis Kevrekidis, Marisa Hughes, Thomas Haine, Marie-Aude Pradal, Renske Gelderloos, Caroline Tang, Anshu Saksena, Larry White

We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC).

Question Answering

Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity

no code implementations28 Nov 2022 Nathan Drenkow, Alvin Tan, Chace Ashcraft, Kiran Karra

The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e. g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions).

Image Classification

Latent Properties of Lifelong Learning Systems

no code implementations28 Jul 2022 Corban Rivera, Chace Ashcraft, Alexander New, James Schmidt, Gautam Vallabha

Creating artificial intelligence (AI) systems capable of demonstrating lifelong learning is a fundamental challenge, and many approaches and metrics have been proposed to analyze algorithmic properties.

reinforcement-learning Reinforcement Learning (RL)

L2Explorer: A Lifelong Reinforcement Learning Assessment Environment

1 code implementation14 Mar 2022 Erik C. Johnson, Eric Q. Nguyen, Blake Schreurs, Chigozie S. Ewulum, Chace Ashcraft, Neil M. Fendley, Megan M. Baker, Alexander New, Gautam K. Vallabha

Despite groundbreaking progress in reinforcement learning for robotics, gameplay, and other complex domains, major challenges remain in applying reinforcement learning to the evolving, open-world problems often found in critical application spaces.

Continual Learning reinforcement-learning +2

Machine Learning aided Crop Yield Optimization

no code implementations1 Nov 2021 Chace Ashcraft, Kiran Karra

We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield.

BIG-bench Machine Learning OpenAI Gym +2

SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks

no code implementations9 Sep 2021 Kiran Karra, Chace Ashcraft, Cash Costello

Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn generalized underlying structure.

Data Augmentation Self-Supervised Learning

Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers

no code implementations14 Jun 2021 Chace Ashcraft, Kiran Karra

In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents.

Data Poisoning Multi-Task Learning +2

PICO: Primitive Imitation for COntrol

no code implementations22 Jun 2020 Corban G. Rivera, Katie M. Popek, Chace Ashcraft, Edward W. Staley, Kapil D. Katyal, Bart L. Paulhamus

In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO.

Imitation Learning PICO

The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models

1 code implementation13 Mar 2020 Kiran Karra, Chace Ashcraft, Neil Fendley

In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL) models with trojans at scale.

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