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Found 12 papers, 2 papers with code
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations
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
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.
On the Equivalence of Graph Convolution and Mixup
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.
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
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
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.
A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
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
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
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.
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Subgraph reasoning with message passing is a promising and popular solution.
A Primer on Deep Learning for Causal Inference
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework.
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
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.
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method
In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation.
