498 citations · 1.8k across the 20 of their papers we have counts for
21 papers
Maximum Likelihood Particle Tracking in Turbulent Flows via Sparse Optimization
Griffin M Kearney, Kasey M Laurent, Makan Fardad
Lagrangian particle tracking is essential for characterizing turbulent flows, but inferring particle acceleration from inherently noisy position data remains a significant challeng…
Adaptive Clutter Suppression via Convex Optimization
Yifan He, Griffin Kearney, Makan Fardad
Passive and bistatic radar systems are often limited by strong clutter and direct-path interference that mask weak moving targets. Conventional cancellation methods such as the ext…
Optimization Based Data Enrichment Using Stochastic Dynamical System Models
Griffin M. Kearney, Makan Fardad
We develop a general framework for state estimation in systems modeled with noise-polluted continuous time dynamics and discrete time noisy measurements. Our approach is based on m…
Loss Attitude Aware Energy Management for Signal Detection
Baocheng Geng, Chen Quan, Tianyun Zhang +2
This work considers a Bayesian signal processing problem where increasing the power of the probing signal may cause risks or undesired consequences. We employ a market based approa…
Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting
Minghai Qin, Tianyun Zhang, Fei Sun +4
Deep neural networks (DNNs) have shown to provide superb performance in many real life applications, but their large computation cost and storage requirement have prevented them fr…
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
Tianyun Zhang, Xiaolong Ma, Zheng Zhan +7
To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categ…
Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang, Tianyun Zhang, Sijia Liu +4
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing ad…
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Shaokai Ye, Xiaoyu Feng, Tianyun Zhang +11
Weight pruning and weight quantization are two important categories of DNN model compression. Prior work on these techniques are mainly based on heuristics. A recent work developed…
Progressive Weight Pruning of Deep Neural Networks using ADMM
Shaokai Ye, Tianyun Zhang, Kaiqi Zhang +10
Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices…
StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang +8
Weight pruning methods of DNNs have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage require…
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang +4
Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarante…
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
Tianyun Zhang, Shaokai Ye, Yipeng Zhang +2
We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning…
A Memristor-Based Optimization Framework for AI Applications
Sijia Liu, Yanzhi Wang, Makan Fardad +1
Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promisin…
Optimized Sensor Collaboration for Estimation of Temporally Correlated Parameters
Sijia Liu, Swarnendu Kar, Makan Fardad +1
In this paper, we aim to design the optimal sensor collaboration strategy for the estimation of time-varying parameters, where collaboration refers to the act of sharing measuremen…
Sensor Selection for Estimation with Correlated Measurement Noise
Sijia Liu, Sundeep Prabhakar Chepuri, Makan Fardad +3
In this paper, we consider the problem of sensor selection for parameter estimation with correlated measurement noise. We seek optimal sensor activations by formulating an optimiza…
Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation
Sijia Liu, Swarnendu Kar, Makan Fardad +1
In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmi…
Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems
Sijia Liu, Makan Fardad, Engin Masazade +1
We consider the problem of finding optimal time-periodic sensor schedules for estimating the state of discrete-time dynamical systems. We assume that {multiple} sensors have been d…
Algorithms for leader selection in stochastically forced consensus networks
Fu Lin, Makan Fardad, Mihailo R. Jovanović
We are interested in assigning a pre-specified number of nodes as leaders in order to minimize the mean-square deviation from consensus in stochastically forced networks. This prob…
Design of optimal sparse interconnection graphs for synchronization of oscillator networks
Makan Fardad, Fu Lin, Mihailo R. Jovanović
We study the optimal design of a conductance network as a means for synchronizing a given set of oscillators. Synchronization is achieved when all oscillator voltages reach consens…
Optimal Control of Vehicular Formations with Nearest Neighbor Interactions
Fu Lin, Makan Fardad, Mihailo R. Jovanović
We consider the design of optimal localized feedback gains for one-dimensional formations in which vehicles only use information from their immediate neighbors. The control objecti…
Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers
Fu Lin, Makan Fardad, Mihailo R. Jovanović
We design sparse and block sparse feedback gains that minimize the variance amplification (i.e., the norm) of distributed systems. Our approach consists of two steps. First,…