1.1k citations · 2.3k across the 33 of their papers we have counts for
33 papers
Leveraging 3D Technologies for Hardware Security: Opportunities and Challenges
Peng Gu, Shuangchen Li, Dylan Stow +4
3D die stacking and 2.5D interposer design are promising technologies to improve integration density, performance and cost. Current approaches face serious issues in dealing with e…
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts
Dongbo Wang, Chang Liu, Zhixiao Zhao +8
In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational model…
On a neural network approach for solving potential control problem of the semiclassical Schrödinger equation
Yating Wang, Liu Liu
Robust control design for quantum systems is a challenging and key task for practical technology. In this work, we apply neural networks to learn the control problem for the semicl…
One-shot neural band selection for spectral recovery
Hai-Miao Hu, Zhenbo Xu, Wenshuai Xu +5
Band selection has a great impact on the spectral recovery quality. To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit cluste…
Compact and High-Performance TCAM Based on Scaled Double-Gate FeFETs
Liu Liu, Shubham Kumar, Simon Thomann +3
Ternary content addressable memory (TCAM), widely used in network routers and high-associativity caches, is gaining popularity in machine learning and data-analytic applications. F…
Convergence of the Fourier-Galerkin spectral method for the Boltzmann equation with uncertainties
Liu Liu, Kunlun Qi
It is well-known that the Fourier-Galerkin spectral method has been a popular approach for the numerical approximation of the deterministic Boltzmann equation with spectral accurac…
ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs
Guyue Huang, Yang Bai, Liu Liu +4
Pipelining between data loading and computation is a critical tensor program optimization for GPUs. In order to unleash the high performance of latest GPUs, we must perform a syner…
Dynamic N:M Fine-grained Structured Sparse Attention Mechanism
Zhaodong Chen, Yuying Quan, Zheng Qu +3
Transformers are becoming the mainstream solutions for various tasks like NLP and Computer vision. Despite their success, the high complexity of the attention mechanism hinders the…
AKB-48: A Real-World Articulated Object Knowledge Base
Liu Liu, Wenqiang Xu, Haoyuan Fu +3
Human life is populated with articulated objects. A comprehensive understanding of articulated objects, namely appearance, structure, physics property, and semantics, will benefit…
iSeg3D: An Interactive 3D Shape Segmentation Tool
Sucheng Qian, Liu Liu, Wenqiang Xu +1
A large-scale dataset is essential for learning good features in 3D shape understanding, but there are only a few datasets that can satisfy deep learning training. One of the major…
Encoding Selection for Solving Hamiltonian Cycle Problems with ASP
Liu Liu, Miroslaw Truszczynski
It is common for search and optimization problems to have alternative equivalent encodings in ASP. Typically none of them is uniformly better than others when evaluated on broad cl…
FurcaNet: An end-to-end deep gated convolutional, long short-term memory, deep neural networks for single channel speech separation
Ziqiang Shi, Huibin Lin, Liu Liu +4
Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the…
Is CQT more suitable for monaural speech separation than STFT? an empirical study
Ziqiang Shi, Huibin Lin, Liu Liu +2
Short-time Fourier transform (STFT) is used as the front end of many popular successful monaural speech separation methods, such as deep clustering (DPCL), permutation invariant tr…
High Dimensional Robust -Estimation: Arbitrary Corruption and Heavy Tails
Liu Liu, Tianyang Li, Constantine Caramanis
We consider the problem of sparsity-constrained -estimation when both explanatory and response variables have heavy tails (bounded 4-th moments), or a fraction of arbitrary corr…
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…
A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification
Ziqiang Shi, Mengjiao Wang, Liu Liu +2
J-vector has been proved to be very effective in text-dependent speaker verification with short-duration speech. However, the current state-of-the-art back-end classifiers, e.g. jo…