138 citations · 295 across the 10 of their papers we have counts for
10 papers
Reducing the Complexity of Matrix Multiplication by Quantum Computing
Jiaqi Yao, Tianjian Huang, Tonghe Zhang +1
Matrix multiplication is a fundamental operation in compute-intensive tasks and a key component of modern quantum acceleration frameworks. Here we present a quantum matrix multipli…
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing
Zhehui Wang, Benjamin Chen Ming Choong, Tian Huang +4
Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods suc…
Universal Matrix Multiplication on Quantum Computer
Jiaqi Yao, Tianjian Huang, Zipeng Cai +1
As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directly determines the training and inferenc…
Optimal Differentially Private Model Training with Public Data
Andrew Lowy, Zeman Li, Tianjian Huang +1
Differential privacy (DP) ensures that training a machine learning model does not leak private data. In practice, we may have access to auxiliary public data that is free of privac…
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
Daniel Lundstrom, Tianjian Huang, Meisam Razaviyayn
As deep learning (DL) efficacy grows, concerns for poor model explainability grow also. Attribution methods address the issue of explainability by quantifying the importance of an…
Robustness through Data Augmentation Loss Consistency
Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar +5
While deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks, ERM is not robust to distribution sh…
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
Babak Barazandeh, Tianjian Huang, George Michailidis
Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs). However, most o…
Alternating Direction Method of Multipliers for Quantization
Tianjian Huang, Prajwal Singhania, Maziar Sanjabi +2
Quantization of the parameters of machine learning models, such as deep neural networks, requires solving constrained optimization problems, where the constraint set is formed by t…
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances
Meisam Razaviyayn, Tianjian Huang, Songtao Lu +3
The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games. Given a class…
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Maher Nouiehed, Maziar Sanjabi, Tianjian Huang +2
Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. This problem has been extensively studied in the convex-c…