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Found 13 papers, 2 papers with code
Difference Learning for Air Quality Forecasting Transport Emulation
Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events.
Neuro-Symbolic Bi-Directional Translation -- Deep Learning Explainability for Climate Tipping Point Research
In this work we propose a neuro-symbolic approach called Neuro-Symbolic Question-Answer Program Translator, or NS-QAPT, to address explainability and interpretability for deep learning climate simulation, applied to climate tipping point discovery.
A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)
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
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).
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity
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).
Latent Properties of Lifelong Learning Systems
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.
L2Explorer: A Lifelong Reinforcement Learning Assessment Environment
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.
Meta Arcade: A Configurable Environment Suite for Meta-Learning
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time.
Machine Learning aided Crop Yield Optimization
We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield.
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks
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.
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents.
PICO: Primitive Imitation for COntrol
In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO.
The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models
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.
