no code implementations • 21 Dec 2023 • Yang Lv, Brandon R. Zink, Robert P. Bloom, Hüsrev Cılasun, Pravin Khanal, Salonik Resch, Zamshed Chowdhury, Ali Habiboglu, Weigang Wang, Sachin S. Sapatnekar, Ulya Karpuzcu, Jian-Ping Wang
Based on the experimental results, a suite of modeling has been developed to characterize the accuracy of CRAM computation.
no code implementations • 25 Sep 2019 • Yang Lv, Liangsheng Zhuang, Pengyu Luo
Session based recommendation has become one of the research hotpots in the field of recommendation systems due to its highly practical value. Previous deep learning methods mostly focus on the sequential characteristics within the current session, and neglect the context similarity and temporal similarity between sessions which contain abundant collaborative information. In this paper, we propose a novel neural networks framework, namely Neighborhood Enhanced and Time Aware Recommendation Machine(NETA) for session based recommendation.
no code implementations • 30 Jul 2017 • Hu Chen, Yi Zhang, Yunjin Chen, Junfeng Zhang, Weihua Zhang, Huaiqiaing Sun, Yang Lv, Peixi Liao, Jiliu Zhou, Ge Wang
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on.