1 code implementation • 21 Apr 2024 • Jie Peng, Weiyu Li, Qing Ling
Robustness to malicious attacks is of paramount importance for distributed learning.
no code implementations • 8 Mar 2024 • Zhen Tan, Jie Peng, Tianlong Chen, Huan Liu
Large Language Models (LLMs) have catalyzed transformative advances across a spectrum of natural language processing tasks through few-shot or zero-shot prompting, bypassing the need for parameter tuning.
no code implementations • 20 Oct 2023 • Wenhao Yu, Jie Peng, Quecheng Qiu, Hanyu Wang, Lu Zhang, Jianmin Ji
However, two roadblocks arise for training a DRL policy that outputs paths: (1) The action space for potential paths often involves higher dimensions comparing to low-level commands, which increases the difficulties of training; (2) It takes multiple time steps to track a path instead of a single time step, which requires the path to predicate the interactions of the robot w. r. t.
no code implementations • 10 Aug 2023 • Jie Peng, Weiyu Li, Qing Ling
Motivated by this observation, we introduce two variance reduction methods, stochastic average gradient algorithm (SAGA) and loopless stochastic variance-reduced gradient (LSVRG), to Byzantine-robust decentralized stochastic optimization for eliminating the negative effect of the stochastic gradient noise.
no code implementations • 22 Mar 2023 • Guoliang You, Xiaomeng Chu, Yifan Duan, Jie Peng, Jianmin Ji, Yu Zhang, Yanyong Zhang
In particular, we specify a prompt-transformer for representation conversion and propose a two-step training process to train the prompt-transformer for the target environment, while the rest of the DRL pipeline remains unchanged.
no code implementations • 22 Mar 2023 • Yuan Chen, Quecheng Qiu, Xiangyu Liu, Guangda Chen, Shunyi Yao, Jie Peng, Jianmin Ji, Yanyong Zhang
The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization.
1 code implementation • 7 Nov 2022 • Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang
Instead of extracting features from the tensor program itself, TLP extracts features from the schedule primitives.
1 code implementation • 7 Jun 2022 • Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui
shifts in prediction mechanisms ($Y|X$-shifts).
1 code implementation • 13 Aug 2021 • Yu'an Chen, Ruosong Ye, Ziyang Tao, Hongjian Liu, Guangda Chen, Jie Peng, Jun Ma, Yu Zhang, Jianmin Ji, Yanyong Zhang
Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, by directly mapping perception inputs into robot control commands.
no code implementations • 22 Feb 2021 • Jie Peng, Juncong Zheng, Jing Yu, Pinghua Tang, G. Alvarado Barrios, Jianxin Zhong, Enrique Solano, F. Albarran-Arriagada, Lucas Lamata
General solutions to the quantum Rabi model involve subspaces with unbounded number of photons.
Quantum Physics Optics
no code implementations • 27 Jan 2021 • Lijing Zheng, Haibin Kan, Yanjun Li, Jie Peng, Deng Tang
With the help of this characterization, we obtain an infinite family of APN functions for $ n=2m $ with $m$ being an odd positive integer: $ f(x)=a{\rm Tr}^{n}_{m}(bx^3)+a^q{\rm Tr}^{n}_{m}(b^3x^9) $, where $ a\in \mathbb{F}_{2^n}$ such that $ a+a^q\neq 0 $ and $ b $ is a non-cube in $ \mathbb{F}_{2^n} $.
Information Theory Information Theory
2 code implementations • 17 Sep 2020 • Jie Peng, Zhaoxian Wu, Qing Ling, Tianyi Chen
We prove that the proposed method reaches a neighborhood of the optimal solution at a linear convergence rate and the learning error is determined by the number of Byzantine workers.
1 code implementation • 12 May 2020 • Jie Peng, Weiyu Li, Qing Ling
In this paper, we consider the Byzantine-robust stochastic optimization problem defined over decentralized static and time-varying networks, where the agents collaboratively minimize the summation of expectations of stochastic local cost functions, but some of the agents are unreliable due to data corruptions, equipment failures or cyber-attacks.
2 code implementations • 11 Apr 2018 • Jilei Yang, Jie Peng
In this paper, we study time-varying graphical models based on data measured over a temporal grid.
no code implementations • 9 Jun 2014 • Ru Wang, Jie Peng
Specifically, an ensemble of DAGs is first learned based on bootstrap resamples of the data and then an aggregated DAG is derived by minimizing the overall distance to the entire ensemble.