no code implementations • 21 Oct 2024 • Da Ju, Song Jiang, Andrew Cohen, Aaron Foss, Sasha Mitts, Arman Zharmagambetov, Brandon Amos, Xian Li, Justine T Kao, Maryam Fazel-Zarandi, Yuandong Tian
In this paper, we propose To the Globe (TTG), a real-time demo system that takes natural language requests from users, translates it to symbolic form via a fine-tuned Large Language Model, and produces optimal travel itineraries with Mixed Integer Linear Programming solvers.
no code implementations • 4 Mar 2024 • Hongyan Li, Song Jiang, Wenjun Sun, Liwei Xu, Guanyu Zhou
We develop a Macroscopic Auxiliary Asymptotic-Preserving Neural Network (MA-APNN) method to solve the time-dependent linear radiative transfer equations (LRTEs), which have a multi-scale nature and high dimensionality.
no code implementations • 7 Oct 2023 • Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi, Hamed Firooz, Yinglong Xia, Bugra Akyildiz, Yizhou Sun, Jinchao Li, Qifan Wang, Asli Celikyilmaz
Breakdown analysis further highlights RESPROMPT particularly excels in complex multi-step reasoning: for questions demanding at least five reasoning steps, RESPROMPT outperforms the best CoT based benchmarks by a remarkable average improvement of 21. 1% on LLaMA-65B and 14. 3% on LLaMA2-70B.
1 code implementation • 29 Sep 2023 • Xiaotian Han, Hanqing Zeng, Yu Chen, Shaoliang Nie, Jingzhou Liu, Kanika Narang, Zahra Shakeri, Karthik Abinav Sankararaman, Song Jiang, Madian Khabsa, Qifan Wang, Xia Hu
We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
no code implementations • 24 Jul 2023 • Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Ren Chen, Christopher Leung, Jiajie Tang, Jiebo Luo
Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics.
no code implementations • 20 Jun 2023 • Song Jiang, Zijie Huang, Xiao Luo, Yizhou Sun
We model a multi-agent dynamical system as a graph and propose CounterFactual GraphODE (CF-GODE), a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units.
no code implementations • 11 Dec 2022 • Hongyan Li, Song Jiang, Wenjun Sun, Liwei Xu, Guanyu Zhou
We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs).
no code implementations • 16 Oct 2022 • Ang Li, Song Jiang, Yizhou Sun, Judea Pearl
This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data.
no code implementations • 15 Oct 2022 • Ang Li, Song Jiang, Yizhou Sun, Judea Pearl
In this paper, we present a machine learning framework that uses the bounds of the benefit function that are estimable from the finite population data to learn the bounds of the benefit function for each cell of characteristics.
1 code implementation • 8 Oct 2022 • Yuxia Geng, Jiaoyan Chen, Jeff Z. Pan, Mingyang Chen, Song Jiang, Wen Zhang, Huajun Chen
Subgraph reasoning with message passing is a promising and popular solution.
no code implementations • 9 Oct 2021 • Bernard Koch, Tim Sainburg, Pablo Geraldo, Song Jiang, Yizhou Sun, Jacob Gates Foster
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework.
1 code implementation • NeurIPS 2019 • Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
Original full-batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs.
no code implementations • WS 2017 • Song Jiang, Xiaotian Han
In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation.