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Found 5 papers, 1 papers with code
ESGReveal: An LLM-based approach for extracting structured data from ESG reports
ESGReveal is an innovative method proposed for efficiently extracting and analyzing Environmental, Social, and Governance (ESG) data from corporate reports, catering to the critical need for reliable ESG information retrieval.
AutoPCF: Efficient Product Carbon Footprint Accounting with Large Language Models
The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle.
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning
We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks, including deep neural network (DNN) regression and neural operator learning (DeepONet).
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users.
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization
ARC methods are a relatively new family of optimization strategies that utilize a cubic-regularization (CR) term in place of trust-regions and line-searches.
