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Found 11 papers, 4 papers with code
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model
Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model.
Breaking the Curse of Quality Saturation with User-Centric Ranking
A key puzzle in search, ads, and recommendation is that the ranking model can only utilize a small portion of the vastly available user interaction data.
Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education
This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem.
Time-Series Event Prediction with Evolutionary State Graph
In this paper, we present evolutionary state graph, a dynamic graph structure designed to systematically represent the evolving relations (edges) among states (nodes) along time.
Capturing Evolution Genes for Time Series Data
The modeling of time series is becoming increasingly critical in a wide variety of applications.
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Millions of people worldwide are absent from their country's census.
Representation Learning for Scale-free Networks
Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored.
