4 citations · 11 across the 26 of their papers we have counts for
7 papers · 1 filter
Backward-Consistent Diffusion Sampling for Sparsely Observed PDE Inverse Problems
Yida Pan, Muhammad H. Ashiq, Chanyong Jung +4
Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for which generative machine learning m…
On the Dataless Training of Neural Networks
Alvaro Velasquez, Susmit Jha, Ismail R. Alkhouri
This paper surveys studies on the use of neural networks for optimization in the training-data-free setting. Specifically, we examine the dataless application of neural network arc…
A Dataless Reinforcement Learning Approach to Rounding Hyperplane Optimization for Max-Cut
Gabriel Maliakal, Ismail Alkhouri, Alvaro Velasquez +2
The Maximum Cut (MaxCut) problem is NP-Complete, and obtaining its optimal solution is NP-hard in the worst case. As a result, heuristic-based algorithms are commonly used, though…
Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition
Xitong Zhang, Ismail R. Alkhouri, Rongrong Wang
Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNN…
On the Robustness of AlphaFold: A COVID-19 Case Study
Ismail Alkhouri, Sumit Jha, Andre Beckus +5
Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks…
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks
Ismail R. Alkhouri, George K. Atia, Alvaro Velasquez
The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approache…
BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples
Ismail R. Alkhouri, Alvaro Velasquez, George K. Atia
The design of additive imperceptible perturbations to the inputs of deep classifiers to maximize their misclassification rates is a central focus of adversarial machine learning. A…