466 citations · 1.6k across the 73 of their papers we have counts for
24 papers · 1 filter
Sensitivity-Constrained Neural Operators for Data-Efficient Forward and Inverse Modeling of Partial Differential Equation Systems
Abdolmehdi Behroozi, Chaopeng Shen, Daniel Kifer +1
Neural operators provide fast surrogates for partial differential equation (PDE) solvers, but their reliability can degrade for high-dimensional spatial inputs and inverse or repea…
Correlating Cross-Iteration Noise for DP-SGD using Model Curvature
Xin Gu, Yingtai Xiao, Guanlin He +3
Differentially private stochastic gradient descent (DP-SGD) offers the promise of training deep learning models while mitigating many privacy risks. However, there is currently a l…
StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions
Nicholas Kraabel, Jiangtao Liu, Yuchen Bian +2
Managing natural resources and mitigating risks from floods, droughts, wildfires, and landslides require models that can accurately predict climate-driven land-surface responses. T…
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Abdolmehdi Behroozi, Chaopeng Shen and, Daniel Kifer
Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (…
Efficient and Private Marginal Reconstruction with Local Non-Negativity
Brett Mullins, Miguel Fuentes, Yingtai Xiao +3
Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private…
Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network
Neisarg Dave, Daniel Kifer, C. Lee Giles +1
This paper analyzes two competing rule extraction methodologies: quantization and equivalence query. We trained RNN models, extracting DFA with a quantization approa…