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Found 8 papers, 2 papers with code
Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation
The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation.
Pattern formation in a predator-prey model with Allee effect and hyperbolic mortality on networked and non-networked environments
In this paper, we establish a predator-prey model with weak Allee effect, analyze and verify the Turing instability conditions on the large ER (Erd\"{o}s-R\'{e}nyi) random network with the help of Turing stability theory and numerical experiments, and obtain the Turing instability region.
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
Previous researches found that in-context learning is an effective approach to exploiting LLM, by using a few task-related labeled data as demonstration examples to construct a few-shot prompt for answering new questions.
Pattern formation of parasite-host model induced by fear effect
In this paper, based on the epidemiological microparasite model, a parasite-host model is established by considering the fear effect of susceptible individuals on infectors.
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework.
Ranked #10 on
Question Answering
on OpenBookQA
Assisted Learning for Organizations with Limited Imbalanced Data
In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance.
ASCII: ASsisted Classification with Ignorance Interchange
In this paper, we propose a method named ASCII for an agent to improve its classification performance through assistance from other agents.
Model Linkage Selection for Cooperative Learning
The main crux is to sequentially incorporate additional learners that can enhance the prediction accuracy of an existing joint model based on user-specified parameter sharing patterns across a set of learners.
