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Found 7 papers, 0 papers with code
MRC-based Medical NER with Multi-task Learning and Multi-strategies
We integrate MRC-CRF model for SL and MRC-Biaffine model for SBD into the multi-task learning architecture, and select the more efficient MRC-CRF as the final decoder.
Deconfounding Time Series Forecasting
Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making.
Mitigating Dual Latent Confounding Biases in Recommender Systems
Traditional recommender systems, however, are complicated by confounding bias, particularly in the presence of latent confounders that affect both item exposure and user feedback.
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
As network data applications continue to expand, causal inference within networks has garnered increasing attention.
A Deconfounding Approach to Climate Model Bias Correction
This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders.
Estimating Peer Direct and Indirect Effects in Observational Network Data
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions.
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records.
