Search Results for author: Xiren Zhou

Found 7 papers, 1 papers with code

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

1 code implementation22 Apr 2024 Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Qin Cai, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Dong Chen, Dongdong Chen, Yen-Chun Chen, Yi-Ling Chen, Parul Chopra, Xiyang Dai, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Victor Fragoso, Dan Iter, Mei Gao, Min Gao, Jianfeng Gao, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Yunsheng Li, Chen Liang, Lars Liden, Ce Liu, Mengchen Liu, Weishung Liu, Eric Lin, Zeqi Lin, Chong Luo, Piyush Madan, Matt Mazzola, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Swadheen Shukla, Xia Song, Masahiro Tanaka, Andrea Tupini, Xin Wang, Lijuan Wang, Chunyu Wang, Yu Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Haiping Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, Weijian Xu, Sonali Yadav, Fan Yang, Jianwei Yang, ZiYi Yang, Yifan Yang, Donghan Yu, Lu Yuan, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou

We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.

Language Modelling

Underground Diagnosis Based on GPR and Learning in the Model Space

no code implementations25 Nov 2022 Ao Chen, Xiren Zhou, Yizhan Fan, Huanhuan Chen

In this paper, a GPR B-scan image diagnosis method based on learning in the model space is proposed.

GPR

Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data

no code implementations21 Oct 2022 Xiren Zhou, Shikang Liu, Ao Chen, Yizhan Fan, Huanhuan Chen

In this paper, a novel method is proposed to improve the subsurface anomaly detection from GPR B-scan images.

Anomaly Detection GPR

Mapping the Buried Cable by Ground Penetrating Radar and Gaussian-Process Regression

no code implementations25 Jan 2022 Xiren Zhou, Qiuju Chen, Shengfei Lyu, Huanhuan Chen

On the basis of the established coordinate system and the derived points on the cable, the clustering method and cable fitting algorithm based on Gaussian-process regression are proposed to find the most likely locations of the underground cables.

GPR regression

Estimating the Direction and Radius of Pipe from GPR Image by Ellipse Inversion Model

no code implementations25 Jan 2022 Xiren Zhou, Qiuju Chen, Shengfei Lyu, Huanhuan Chen

By minimizing the sum of the algebraic distances from the extracted point set to the inverted ellipse, the most likely pipeline's direction and radius are determined.

GPR

Rethink AI-based Power Grid Control: Diving Into Algorithm Design

no code implementations23 Dec 2020 Xiren Zhou, Siqi Wang, Ruisheng Diao, Desong Bian, Jiahui Duan, Di Shi

Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain. In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering. To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process.

Imitation Learning reinforcement-learning +1

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