Search Results for author: Song Jiang

Found 13 papers, 3 papers with code

To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning

no code implementations21 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.

Language Modelling Large Language Model

Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations

no code implementations4 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.

Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models

no code implementations7 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.

Math

On the Equivalence of Graph Convolution and Mixup

1 code implementation29 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.

Data Augmentation Graph Neural Network

LLM-Rec: Personalized Recommendation via Prompting Large Language Models

no code implementations24 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.

CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

no code implementations20 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.

Causal Inference counterfactual

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations

no code implementations11 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).

Learning Probabilities of Causation from Finite Population Data

no code implementations16 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.

Unit Selection: Learning Benefit Function from Finite Population Data

no code implementations15 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.

A Primer on Deep Learning for Causal Inference

no code implementations9 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.

Causal Inference Deep Learning

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

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.

Node Classification

DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method

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.

Emotion Classification Emotion Recognition +2

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