1.1k citations · 2.3k across the 33 of their papers we have counts for
9 papers · 1 filter
Dynamic Sparse Graph for Efficient Deep Learning
Liu Liu, Lei Deng, Xing Hu +4
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference…
Stochastically Controlled Stochastic Gradient for the Convex and Non-convex Composition problem
Liu Liu, Ji Liu, Cho-Jui Hsieh +1
In this paper, we consider the convex and non-convex composition problem with the structure , where $G( x )=\frac{1}{n}\sum\…
High-Performance Hybrid Silicon and Lithium Niobate Mach-Zehnder Modulators for 100 Gbit/s and Beyond
Mingbo He, Mengyue Xu, Yuxuan Ren +13
Optical modulators are at the heart of optical communication links. Ideally, they should feature low insertion loss, low drive voltage, large modulation bandwidth, high linearity,…
Stochastic Zeroth-order Optimization via Variance Reduction method
Liu Liu, Minhao Cheng, Cho-Jui Hsieh +1
Derivative-free optimization has become an important technique used in machine learning for optimizing black-box models. To conduct updates without explicitly computing gradient, m…
High Dimensional Robust Sparse Regression
Liu Liu, Yanyao Shen, Tianyang Li +1
We provide a novel -- and to the best of our knowledge, the first -- algorithm for high dimensional sparse regression with constant fraction of corruptions in explanatory and/or re…
Approximate Newton-based statistical inference using only stochastic gradients
Tianyang Li, Anastasios Kyrillidis, Liu Liu +1
We present a novel statistical inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion…
Discriminative Cross-View Binary Representation Learning
Liu Liu, Hairong Qi
Learning compact representation is vital and challenging for large scale multimedia data. Cross-view/cross-modal hashing for effective binary representation learning has received s…
PIRT: A Runtime Framework to Enable Energy-Efficient Real-Time Robotic Applications on Heterogeneous Architectures
Liu Liu, Shaoshan Liu, Zhe Zhang +3
Enabling full robotic workloads with diverse behaviors on mobile systems with stringent resource and energy constraints remains a challenge. In recent years, attempts have been mad…
A stochastic asymptotic-preserving scheme for the bipolar semiconductor Boltzmann-Poisson system with random inputs and diffusive scalings
Liu Liu
In this paper, we study the bipolar Boltzmann-Poisson model, both for the deterministic system and the system with uncertainties, with asymptotic behavior leading to the drift diff…