no code implementations • 9 Dec 2024 • Yu Zhong, Rui Zhang, Zihao Zhang, Shuo Wang, Chuan Fang, Xishan Zhang, Jiaming Guo, Shaohui Peng, Di Huang, Yanyang Yan, Xing Hu, Ping Tan, Qi Guo
Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions.
1 code implementation • 16 Aug 2024 • Lei Huang, Jiaming Guo, Guanhua He, Xishan Zhang, Rui Zhang, Shaohui Peng, Shaoli Liu, Tianshi Chen
This dataset is then utilized to fine-tune the LLM, aiming for excelsior generation performance.
no code implementations • 15 Jul 2024 • Yang Zhao, Di Huang, Chongxiao Li, Pengwei Jin, Ziyuan Nan, TianYun Ma, Lei Qi, Yansong Pan, Zhenxing Zhang, Rui Zhang, Xishan Zhang, Zidong Du, Qi Guo, Xing Hu, Yunji Chen
Instruction-tuned large language models (LLMs) have demonstrated remarkable performance in automatically generating code for general-purpose programming languages like Python.
1 code implementation • 8 Jul 2024 • Yutong Wu, Di Huang, Wenxuan Shi, Wei Wang, Lingzhe Gao, Shihao Liu, Ziyuan Nan, Kaizhao Yuan, Rui Zhang, Xishan Zhang, Zidong Du, Qi Guo, Yewen Pu, Dawei Yin, Xing Hu, Yunji Chen
Recent advancements in open-source code large language models (LLMs) have been driven by fine-tuning on the data generated from powerful closed-source LLMs, which are expensive to obtain.
no code implementations • 24 May 2024 • Yuxuan Guo, Shaohui Peng, Jiaming Guo, Di Huang, Xishan Zhang, Rui Zhang, Yifan Hao, Ling Li, Zikang Tian, Mingju Gao, Yutai Li, Yiming Gan, Shuai Liang, Zihao Zhang, Zidong Du, Qi Guo, Xing Hu, Yunji Chen
In this work, we introduce autonomous embodied verification techniques for agents to fill the gap, laying the groundwork for creative tasks.
1 code implementation • NeurIPS 2023 • Shangtong Gui, Chenze Shao, Zhengrui Ma, Xishan Zhang, Yunji Chen, Yang Feng
Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation.
1 code implementation • 21 Jun 2023 • Shuyao Cheng, Pengwei Jin, Qi Guo, Zidong Du, Rui Zhang, Yunhao Tian, Xing Hu, Yongwei Zhao, Yifan Hao, Xiangtao Guan, Husheng Han, Zhengyue Zhao, Ximing Liu, Ling Li, Xishan Zhang, Yuejie Chu, Weilong Mao, Tianshi Chen, Yunji Chen
By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours.
1 code implementation • 12 Jun 2023 • Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Yunkai Gao, Kaizhao Yuan, Ruizhi Chen, Siming Lan, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen
Domain adaptation in reinforcement learning (RL) mainly deals with the changes of observation when transferring the policy to a new environment.
no code implementations • 28 Feb 2023 • Chang Liu, Rui Zhang, Xishan Zhang, Yifan Hao, Zidong Du, Xing Hu, Ling Li, Qi Guo
The energy-efficient works try to decrease the precision of multiplication or replace the multiplication with energy-efficient operations such as addition or bitwise shift, to reduce the energy consumption of FP32 multiplications.
no code implementations • 13 Oct 2022 • Shaohui Peng, Xing Hu, Rui Zhang, Ke Tang, Jiaming Guo, Qi Yi, Ruizhi Chen, Xishan Zhang, Zidong Du, Ling Li, Qi Guo, Yunji Chen
To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments.
no code implementations • 13 Oct 2022 • Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen
Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL.
no code implementations • ICLR 2022 • Di Huang, Rui Zhang, Xing Hu, Xishan Zhang, Pengwei Jin, Nan Li, Zidong Du, Qi Guo, Yunji Chen
In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.
1 code implementation • NeurIPS 2021 • Zhixing Du, Rui Zhang, Ming Chang, Xishan Zhang, Shaoli Liu, Tianshi Chen, Yunji Chen
Second, these methods imitate some features which are mistakenly regarded as the background by the teacher detector.
no code implementations • 29 Sep 2021 • Qi Yi, Jiaming Guo, Rui Zhang, Shaohui Peng, Xing Hu, Xishan Zhang, Ke Tang, Zidong Du, Qi Guo, Yunji Chen
Deep Reinforcement Learning (deep RL) has been successfully applied to solve various decision-making problems in recent years.
no code implementations • 4 Sep 2021 • Ruizhi Chen, Xiaoyu Wu, Yansong Pan, Kaizhao Yuan, Ling Li, TianYun Ma, JiYuan Liang, Rui Zhang, Kai Wang, Chen Zhang, Shaohui Peng, Xishan Zhang, Zidong Du, Qi Guo, Yunji Chen
In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research.
1 code implementation • 26 Jul 2021 • Jiaming Guo, Rui Zhang, Xishan Zhang, Shaohui Peng, Qi Yi, Zidong Du, Xing Hu, Qi Guo, Yunji Chen
In this paper, we propose to replace the state value function with a novel hindsight value function, which leverages the information from the future to reduce the variance of the gradient estimate for stochastic dynamic environments.
no code implementations • CVPR 2021 • Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, Yangyang Xia, Xishan Zhang, Shaoli Liu
The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one.
no code implementations • CVPR 2020 • Xishan Zhang, Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo, Qi Guo, Zidong Du, Tian Zhi, Yunji Chen
Recent emerged quantization technique (i. e., using low bit-width fixed-point data instead of high bit-width floating-point data) has been applied to inference of deep neural networks for fast and efficient execution.
no code implementations • 3 Feb 2020 • Di Huang, Xishan Zhang, Rui Zhang, Tian Zhi, Deyuan He, Jiaming Guo, Chang Liu, Qi Guo, Zidong Du, Shaoli Liu, Tianshi Chen, Yunji Chen
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
no code implementations • 1 Nov 2019 • Xishan Zhang, Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo, Yu Kang, Qi Guo, Zidong Du, Yunji Chen
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
no code implementations • CVPR 2017 • Xishan Zhang, Ke Gao, Yongdong Zhang, Dongming Zhang, Jintao Li, Qi Tian
This paper contributes to: 1)The first in-depth study of the weakness inherent in data-driven static fusion methods for video captioning.