2 code implementations • 14 Nov 2024 • Liwei Ni, Rui Wang, Miao Liu, Xingyu Meng, Xiaoze Lin, Junfeng Liu, Guojie Luo, Zhufei Chu, Weikang Qian, Xiaoyan Yang, Biwei Xie, Xingquan Li, Huawei Li
This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning~(ML) applications within the logic synthesis process.
no code implementations • 11 Apr 2024 • Jianqiang Xiao, Weiwen Guo, Junfeng Liu, Mengze Li
In the field of computer vision, data augmentation is widely used to enrich the feature complexity of training datasets with deep learning techniques.
no code implementations • 18 Mar 2024 • Qinghua Zhao, Jiaang Li, Lei LI, Zenghui Zhou, Junfeng Liu
Existing works have studied the impacts of the order of words within natural text.
no code implementations • 22 Dec 2023 • Liwei Ni, Zonglin Yang, Jiaxi Zhang, Junfeng Liu, Huawei Li, Biwei Xie, Xinquan Li
Rewriting is a common procedure in logic synthesis aimed at improving the performance, power, and area (PPA) of circuits.
1 code implementation • 1 Dec 2023 • Junfeng Liu, Zhuocheng Mei, Kewen Peng, Ranga Raju Vatsavai
However, existing methods are still not able to effectively and efficiently exploit relevant information from these auxiliary supplements to further unleash the power of the conversational agents and the language models they use.
1 code implementation • 4 Nov 2023 • Junfeng Liu, Min Zhou, Shuai Ma, Lujia Pan
Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks.
no code implementations • 28 Sep 2023 • Junfeng Liu, Christopher Symons, Ranga Raju Vatsavai
Recent advances in machine learning and deep learning have led to the widespread use of Conversational AI in many practical applications.
no code implementations • 4 Dec 2022 • Junfeng Liu, Christopher Symons, Ranga Raju Vatsavai
First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information.
1 code implementation • ICLR 2022 • Peifeng Wang, Jonathan Zamora, Junfeng Liu, Filip Ilievski, Muhao Chen, Xiang Ren
In this paper, we propose an Imagine-and-Verbalize (I&V) method, which learns to imagine a relational scene knowledge graph (SKG) with relations between the input concepts, and leverage the SKG as a constraint when generating a plausible scene description.
no code implementations • 1 Oct 2021 • Yun Zhao, Yuqing Wang, Junfeng Liu, Haotian Xia, Zhenni Xu, Qinghang Hong, Zhiyang Zhou, Linda Petzold
In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training.
no code implementations • 14 Jan 2021 • Junfeng Liu, Yiwen Pan, Hong-Hao Zhang
Finally we propose a q-Virasoro construction for the superconformal indices.
High Energy Physics - Theory
no code implementations • 6 Jan 2020 • Xing Zhao, Manos Papagelis, Aijun An, Bao Xin Chen, Junfeng Liu, Yonggang Hu
To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement.
no code implementations • 5 Nov 2019 • Shima Khoshraftar, Sedigheh Mahdavi, Aijun An, Yonggang Hu, Junfeng Liu
To handle large dynamic networks in downstream applications such as link prediction and anomaly detection, it is essential for such networks to be transferred into a low dimensional space.
no code implementations • 16 Aug 2019 • Xing Zhao, Aijun An, Junfeng Liu, Bao Xin Chen
In this paper, we present a distributed paradigm on the parameter server framework called Dynamic Stale Synchronous Parallel (DSSP) which improves the state-of-the-art Stale Synchronous Parallel (SSP) paradigm by dynamically determining the staleness threshold at the run time.
no code implementations • 23 Jan 2018 • Yicheng He, Junfeng Liu, Xia Ning
We have developed a new learning-to-rank method, denoted as pLETORg , that predicts drug ranking structures in each cell line via using drug latent vectors and cell line latent vectors.