The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...
Singapore researchers show how adapting pre-trained AI models can solve data scarcity issues in countries with limited ...
Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...
Introduction: Seismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot ...
Purpose: This study aimed to explore the effect of the order of two learning methods (one based on implicit and another on explicit learning) on students' enjoyment and ability to acquire motor skills ...
Abstract: Deep learning (DL) requires large amounts of labeled data, which is extremely time-consuming and labor-intensive to obtain for medical image segmentation ...
ABSTRACT: This study focuses on the application of Task-based Language Teaching (TBLT) in Integrated English classroom instruction. As a “learning by doing” language teaching method that emerged in ...
Abstract: This paper proposes a novel multi-task learning approach based on spatial-temporal recurrent imputation network (SRIN) for power system short-term voltage stability (STVS) assessment with ...
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