Search Results for author:
Found 14 papers, 3 papers with code
Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models
In a materials-by-design case study, our model provides state-of-the-art prediction of polymer glass transition temperature.
A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models
Instead, we apply the supervised cadre model (SCM), which does use this metric.
Semantically-aware population health risk analyses
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition.
Lifelong Learning Metrics
The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way.
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.
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.
Curvature-informed multi-task learning for graph networks
Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics.
Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations
We propose an approach to solving partial differential equations (PDEs) using a set of neural networks which we call Neural Basis Functions (NBF).
Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling
While some catastrophic forgetting persisted over the course of network training, higher levels of synaptic downscaling lead to better retention of early tasks and further facilitated the recovery of early task accuracy during subsequent training.
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 (PINNs) to solve increasingly-complex coupled ordinary differential equations (ODEs).
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
Evaluating the diversity and utility of materials proposed by generative models
Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures.
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows
However, high-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space.
Data-efficient operator learning for solving high Mach number fluid flow problems
We consider the problem of using SciML to predict solutions of high Mach fluid flows over irregular geometries.
