20 citations · 43 across the 9 of their papers we have counts for
9 papers
Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
Mohamed Tarek, Pedro Afonso
Nonlinear Mixed Effects (NLME) models are widely used in pharmacometrics and related fields to analyze hierarchical and longitudinal data. However, as the number of parameters and…
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
Abdelwahed Khamis, Russell Tsuchida, Mohamed Tarek +2
Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied ques…
Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization
Mohamed Tarek, Yijiang Huang
Non-convex optimization problems have multiple local optimal solutions. Non-convex optimization problems are commonly found in numerous applications. One of the methods recently pr…
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
Frank Schäfer, Mohamed Tarek, Lyndon White +1
No single Automatic Differentiation (AD) system is the optimal choice for all problems. This means informed selection of an AD system and combinations can be a problem-specific var…
Approximation schemes for stochastic compliance-based topology optimization with many loading scenarios
Mohamed Tarek, Tapabrata Ray
In this paper, approximation schemes are proposed for handling load uncertainty in compliance-based topology optimization problems, where the uncertainty is described in the form o…
Robust and stochastic compliance-based topology optimization with finitely many loading scenarios
Mohamed Tarek, Tapabrata Ray
In this paper, the problem of load uncertainty in compliance problems is addressed where the uncertainty is described in the form of a set of finitely many loading scenarios. Compu…
Bayesian Neural Ordinary Differential Equations
Raj Dandekar, Karen Chung, Vaibhav Dixit +4
Recently, Neural Ordinary Differential Equations has emerged as a powerful framework for modeling physical simulations without explicitly defining the ODEs governing the system, bu…
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Mohamed Tarek, Kai Xu, Martin Trapp +2
We present the preliminary high-level design and features of DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming. Besides a com…
Practical Byte-Granular Memory Blacklisting using Califorms
Hiroshi Sasaki, Miguel A. Arroyo, M. Tarek Ibn Ziad +3
Recent rapid strides in memory safety tools and hardware have improved software quality and security. While coarse-grained memory safety has improved, achieving memory safety at th…