1 code implementation • 30 May 2024 • Seungbeom Hong, Ilmun Kim, Jun Song
In this work, we develop a new theory and method for sufficient dimension reduction (SDR) in single-index models, where SDR is a sub-field of supervised dimension reduction based on conditional independence.
1 code implementation • 26 May 2024 • Dongchen Han, Ziyi Wang, Zhuofan Xia, Yizeng Han, Yifan Pu, Chunjiang Ge, Jun Song, Shiji Song, Bo Zheng, Gao Huang
By exploring the similarities and disparities between the effective Mamba and subpar linear attention Transformer, we provide comprehensive analyses to demystify the key factors behind Mamba's success.
1 code implementation • 24 May 2024 • Chunjiang Ge, Sijie Cheng, ZiMing Wang, Jiale Yuan, Yuan Gao, Jun Song, Shiji Song, Gao Huang, Bo Zheng
To enhance the capabilities of ConvLLaVA, we propose two critical optimizations.
1 code implementation • 18 May 2024 • Xingyu Miao, Haoran Duan, Varun Ojha, Jun Song, Tejal Shah, Yang Long, Rajiv Ranjan
In this work, we propose a novel Trajectory Score Matching (TSM) method that aims to solve the pseudo ground truth inconsistency problem caused by the accumulated error in Interval Score Matching (ISM) when using the Denoising Diffusion Implicit Models (DDIM) inversion process.
no code implementations • 9 Jan 2024 • Qinyi Luo, Penghan Wang, Wei zhang, Fan Lai, Jiachen Mao, Xiaohan Wei, Jun Song, Wei-Yu Tsai, Shuai Yang, Yuxi Hu, Xuehai Qian
Huge embedding tables in modern Deep Learning Recommender Models (DLRM) require prohibitively large memory during training and inference.
no code implementations • 2 Dec 2023 • Xiaohan Bie, Manoj Arthanari, Evelin Barbosa de Melo, Juancheng Li, Stephen Yue, Salim Brahimi, Jun Song
Our findings reveal that lower bainite and tempered martensite exhibit comparable volume percentages of carbides, albeit with a more uniform distribution of carbides in tempered martensite.
no code implementations • 25 Jun 2023 • Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning.
no code implementations • 24 Jun 2023 • Jun Song, William Yang, Chaoyue Zhao
In this paper, we present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem.
no code implementations • 24 Jun 2023 • Jun Song, Chaoyue Zhao
Demand response (DR) has been demonstrated to be an effective method for reducing peak load and mitigating uncertainties on both the supply and demand sides of the electricity market.
no code implementations • 29 Sep 2021 • Jun Song, Chaoyue Zhao, Niao He
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning.
2 code implementations • 5 Jul 2021 • Ali Mahzarnia, Jun Song
In this paper, we propose methods for functional predictor selection and the estimation of smooth functional coefficients simultaneously in a scalar-on-function regression problem under high-dimensional multivariate functional data setting.
1 code implementation • 14 Jun 2020 • Jun Song, Chaoyue Zhao
Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), as the widely employed policy based reinforcement learning (RL) methods, are prone to converge to a sub-optimal solution as they limit the policy representation to a particular parametric distribution class.
no code implementations • 28 Apr 2019 • Jun Song, Ke Han
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution.
no code implementations • 2 Dec 2016 • Jun Song, David A. Moore
We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable.