no code implementations • 28 May 2024 • Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang, Anton Tsitsulin
Retrieval Augmented Generation (RAG) has greatly improved the performance of Large Language Model (LLM) responses by grounding generation with context from existing documents.
no code implementations • 29 Mar 2023 • Jialin Dong, Lin F. Yang
In particular, Du et al. (2020) show that even if a learner is given linear features in $\mathbb{R}^d$ that approximate the rewards in a bandit or RL with a uniform error of $\varepsilon$, searching for an $O(\varepsilon)$-optimal action requires pulling at least $\Omega(\exp(d))$ queries.
no code implementations • 11 Jun 2021 • Jialin Dong, Da Zheng, Lin F. Yang, Geroge Karypis
This global cache allows in-GPU importance sampling of mini-batches, which drastically reduces the number of nodes in a mini-batch, especially in the input layer, to reduce data copy between CPU and GPU and mini-batch computation without compromising the training convergence rate or model accuracy.
no code implementations • 28 Jan 2020 • Jialin Dong, Jun Zhang, Yuanming Shi, Jessie Hui Wang
In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which make a delicate trade-off between exploration and exploitation in coordinate selection.
no code implementations • 12 Nov 2018 • Jialin Dong, Yuanming Shi, Zhi Ding
Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks.
no code implementations • 18 Sep 2018 • Jialin Dong, Yuanming Shi
We consider the problem of demixing a sequence of source signals from the sum of noisy bilinear measurements.