111 citations · 225 across the 24 of their papers we have counts for
6 papers · 1 filter
K-TRAIL: Simulator-Guided Generative Design of EM/RF Circuits
Piyush Saha, Evan Newell, Hanna O'Leary +2
Inverse design of RF and electromagnetic (EM) circuits is challenging because the relationship between circuit layout and electrical response is non-unique, and full-wave simulatio…
RIGID: Robust Linear Regression with Missing Data
Alireza Aghasi, MohammadJavad Feizollahi, Saeed Ghadimi
We present a robust framework to perform linear regression with missing entries in the features. By considering an elliptical data distribution, and specifically a multivariate nor…
Inverse Constrained Reinforcement Learning
Usman Anwar, Shehryar Malik, Alireza Aghasi +1
In real world settings, numerous constraints are present which are hard to specify mathematically. However, for the real world deployment of reinforcement learning (RL), it is crit…
Learning To Solve Differential Equations Across Initial Conditions
Shehryar Malik, Usman Anwar, Ali Ahmed +1
Recently, there has been a lot of interest in using neural networks for solving partial differential equations. A number of neural network-based partial differential equation solve…
Fast Convex Pruning of Deep Neural Networks
Alireza Aghasi, Afshin Abdi, Justin Romberg
We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network. The method is a convex post-processing module, which prunes (sparsifies) a trained…
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Alireza Aghasi, Afshin Abdi, Nam Nguyen +1
We introduce and analyze a new technique for model reduction for deep neural networks. While large networks are theoretically capable of learning arbitrarily complex models, overfi…