Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Xi, Chenguang"'
In recent advancements in Conversational Large Language Models (LLMs), a concerning trend has emerged, showing that many new base LLMs experience a knowledge reduction in their foundational capabilities following Supervised Fine-Tuning (SFT). This pr
Externí odkaz:
http://arxiv.org/abs/2403.02513
The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specialized areas, and exhibiting a decrease in general capabilities when fi
Externí odkaz:
http://arxiv.org/abs/2401.02072
Autor:
Zheng, Shen, Zhang, Yuyu, Zhu, Yijie, Xi, Chenguang, Gao, Pengyang, Zhou, Xun, Chang, Kevin Chen-Chuan
With the rapid advancement of large language models (LLMs), there is a pressing need for a comprehensive evaluation suite to assess their capabilities and limitations. Existing LLM leaderboards often reference scores reported in other papers without
Externí odkaz:
http://arxiv.org/abs/2309.16583
Autor:
Zheng, Zangwei, Xu, Pengtai, Zou, Xuan, Tang, Da, Li, Zhen, Xi, Chenguang, Wu, Peng, Zou, Leqi, Zhu, Yijie, Chen, Ming, Ding, Xiangzhuo, Xue, Fuzhao, Qin, Ziheng, Cheng, Youlong, You, Yang
The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training is critical to ensuring an up-to-date
Externí odkaz:
http://arxiv.org/abs/2204.06240
In this paper, we discuss distributed optimization over directed graphs, where doubly-stochastic weights cannot be constructed. Most of the existing algorithms overcome this issue by applying push-sum consensus, which utilizes column-stochastic weigh
Externí odkaz:
http://arxiv.org/abs/1803.09169
We propose Directed-Distributed Projected Subgradient (D-DPS) to solve a constrained optimization problem over a multi-agent network, where the goal of agents is to collectively minimize the sum of locally known convex functions. Each agent in the ne
Externí odkaz:
http://arxiv.org/abs/1706.07707
This paper considers a distributed optimization problem over a multi-agent network, in which the objective function is a sum of individual cost functions at the agents. We focus on the case when communication between the agents is described by a \emp
Externí odkaz:
http://arxiv.org/abs/1611.06160
Publikováno v:
IEEE Transactions on Automatic Control, vol. 63, no. 5, pp. 1329-1339, May 2018
In this paper, we consider distributed optimization problems where the goal is to minimize a sum of objective functions over a multi-agent network. We focus on the case when the inter-agent communication is described by a strongly-connected, \emph{di
Externí odkaz:
http://arxiv.org/abs/1607.04757
Autor:
Xi, Chenguang, Khan, Usman A.
We propose a distributed algorithm, termed the Directed-Distributed Projected Subgradient (D-DPS), to solve a constrained optimization problem over a multi-agent network, where the goal of agents is to collectively minimize the sum of locally known c
Externí odkaz:
http://arxiv.org/abs/1602.00653
In this paper, we propose a distributed algorithm, called Directed-Distributed Gradient Descent (D-DGD), to solve multi-agent optimization problems over directed graphs. Existing algorithms mostly deal with similar problems under the assumption of un
Externí odkaz:
http://arxiv.org/abs/1510.02146