no code implementations • 21 Mar 2024 • Ziwei Huang, Lu Bai, Mingran Sun, Xiang Cheng
The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time.
no code implementations • 20 Mar 2024 • Zonghui Yang, Shijian Gao, Xiang Cheng, Liuqing Yang
Integrated sensing and communications (ISAC) is a critical enabler for emerging 6G applications, and at its core lies in the dual-functional waveform design.
no code implementations • 15 Mar 2024 • Boxun Liu, Shijian Gao, Zonghui Yang, Xiang Cheng
Integrated Sensing and Communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band.
no code implementations • 15 Feb 2024 • Xiang Cheng, Jingzhao Zhang, Suvrit Sra
We study the task of efficiently sampling from a Gibbs distribution $d \pi^* = e^{-h} d {vol}_g$ over a Riemannian manifold $M$ via (geometric) Langevin MCMC; this algorithm involves computing exponential maps in random Gaussian directions and is efficiently implementable in practice.
no code implementations • 11 Dec 2023 • Xiang Cheng, Yuxin Chen, Suvrit Sra
Many neural network architectures are known to be Turing Complete, and can thus, in principle implement arbitrary algorithms.
no code implementations • 1 Dec 2023 • Yi Yuan, Cheng-Xiang Wang, Xiang Cheng, Bo Ai, David I. Laurenson
Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator.
no code implementations • 4 Oct 2023 • Haotian Zhang, Shijian Gao, Xiang Cheng, Liuqing Yang
The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access.
1 code implementation • 2 Oct 2023 • Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra
Transformer training is notoriously difficult, requiring a careful design of optimizers and use of various heuristics.
1 code implementation • NeurIPS 2023 • Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola
Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet $64 \times 64$.
no code implementations • 25 Jun 2023 • Xiang Cheng, Haotian Zhang, Jianan Zhang, Shijian Gao, Sijiang Li, Ziwei Huang, Lu Bai, Zonghui Yang, Xinhu Zheng, Liuqing Yang
Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review.
no code implementations • 25 Jun 2023 • Xiang Cheng, Ziwei Huang, Lu Bai, Haotian Zhang, Mingran Sun, Boxun Liu, Sijiang Li, Jianan Zhang, Minson Lee
A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research.
no code implementations • NeurIPS 2023 • Xiang Cheng, Bohan Wang, Jingzhao Zhang, Yusong Zhu
However, on the theory side, MCMC algorithms suffer from slow mixing rate when $\pi(x)$ is non-log-concave.
no code implementations • 18 Feb 2023 • Sirui Wu, Jin Lin, Jiarong Li, Feng Liu, Yonghua Song, Yanhui Xu, Xiang Cheng, Zhipeng Yu
Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets.
no code implementations • 31 Oct 2022 • Sangdon Park, Xiang Cheng, Taesoo Kim
Memory-safety bugs introduce critical software-security issues.
no code implementations • 16 Sep 2022 • Xuesong Cai, Xiang Cheng, Fredrik Tufvesson
This article aims at providing insights for a comprehensive understanding of terahertz (THz) propagation channels.
no code implementations • 15 Jun 2021 • Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Xiang Cheng, Bo Xu
Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding.
no code implementations • 23 Dec 2020 • Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet
Conventional wisdom in the sampling literature, backed by a popular diffusion scaling limit, suggests that the mixing time of the Metropolis-Adjusted Langevin Algorithm (MALA) scales as $O(d^{1/3})$, where $d$ is the dimension.
no code implementations • 17 Dec 2020 • Xiang Cheng, Hanchao Yang, Archanaa S Krishnan, Patrick Schaumont, Yaling Yang
To accelerate the laborious manual contact tracing process, digital contact tracing (DCT) tools can track contact events transparently and privately by using the sensing and signaling capabilities of the ubiquitous cell phone.
Cryptography and Security Computers and Society
no code implementations • 11 Dec 2020 • Xiang Cheng, Mitchell Bowden, Bhushan Ramesh Bhange, Priyanka Goyal, Thomas Packer, Faizan Javed
Beyond our application, this TripleLearn framework, as well as the end-to-end process, is model-independent and problem-independent, so it can be generalized to more industrial applications, especially to the eCommerce industry which has similar data foundations and problems.
no code implementations • 10 Oct 2020 • Xiangming Gu, Xiang Cheng
Deep neural networks (DNNs) demonstrate great success in classification tasks.
1 code implementation • 9 Oct 2020 • Tielin Zhang, Shuncheng Jia, Xiang Cheng, Bo Xu
The performance of the proposed BRP-SNN is further verified on the spatial (including MNIST and Cifar-10) and temporal (including TIDigits and DvsGesture) tasks, where the SNN using BRP has reached a similar accuracy compared to other state-of-the-art BP-based SNNs and saved 50% more computational cost than ANNs.
no code implementations • 7 Oct 2020 • Xiang Cheng, Tielin Zhang, Shuncheng Jia, Bo Xu
Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles, hence are playing critical roles in bridging the gap between artificial and natural neural networks.
no code implementations • 4 Dec 2019 • Yunan Zhang, Xiang Cheng, Heting Gao, ChengXiang Zhai
We model the question answering on KG as a cooperative task between two agents, a knowledge graph reasoning agent and an information extraction agent.
no code implementations • 11 Nov 2019 • Yunan Zhang, Xiang Cheng, Yufeng Zhang, Zihan Wang, Zhengqi Fang, Xiaoyan Wang, Zhenya Huang, ChengXiang Zhai
Answering complex questions involving multiple entities and relations is a challenging task.
no code implementations • IJCNLP 2019 • Ruiping Li, Xiang Cheng
Knowledge graphs (KGs) often suffer from sparseness and incompleteness.
no code implementations • ICML 2020 • Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael. I. Jordan
We prove quantitative convergence rates at which discrete Langevin-like processes converge to the invariant distribution of a related stochastic differential equation.
no code implementations • 4 Feb 2019 • Yi-An Ma, Niladri Chatterji, Xiang Cheng, Nicolas Flammarion, Peter Bartlett, Michael. I. Jordan
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional.
no code implementations • 3 Feb 2019 • Xiang Cheng, Peter L. Bartlett, Michael. I. Jordan
In this paper, we quantitative convergence in $W_2$ for a family of Langevin-like stochastic processes that includes stochastic gradient descent and related gradient-based algorithms.
no code implementations • 4 May 2018 • Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, Michael. I. Jordan
We study the problem of sampling from a distribution $p^*(x) \propto \exp\left(-U(x)\right)$, where the function $U$ is $L$-smooth everywhere and $m$-strongly convex outside a ball of radius $R$, but potentially nonconvex inside this ball.
no code implementations • 12 Jul 2017 • Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael. I. Jordan
We study the underdamped Langevin diffusion when the log of the target distribution is smooth and strongly concave.
no code implementations • 25 May 2017 • Xiang Cheng, Peter Bartlett
Langevin diffusion is a commonly used tool for sampling from a given distribution.
no code implementations • 26 May 2016 • Xiang Cheng, Farbod Roosta-Khorasani, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney
We consider first order gradient methods for effectively optimizing a composite objective in the form of a sum of smooth and, potentially, non-smooth functions.
no code implementations • 2 Mar 2016 • Ahmed El Alaoui, Xiang Cheng, Aaditya Ramdas, Martin J. Wainwright, Michael. I. Jordan
Together, these properties show that $p = d+1$ is an optimal choice, yielding a function estimate $\hat{f}$ that is both smooth and non-degenerate, while remaining maximally sensitive to $P$.