IEEE

Enhancing Task-Incremental Learning via a Prompt-Based Hybrid Convolutional Neural Networks (CNNs)-Vision Transformer (ViT) Framework

Abstract: Artificial neural network (ANN) models are widely used in various fields such as image classification, multi-object detection, intent prediction, military applications, and natural language ...
Frontiers

Are S.M.A.R.T goals really smart? The psychological effects of goal-setting in a learning task

Aim: Specific, Measurable, Achievable, Realistic, Time-bound (SMART) goals are commonly used in educational settings as a strategy to optimise learning. However, research and theory suggest that such ...
GitHub

Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework

For a minimal example of how to use the environment framework, refer to examples/simple-calculator. For the environment and training data used in our paper, see AgentBench FC. For reproducing the ...
IEEE

Multi-Task Multi-Agent Reinforcement Learning With Interaction and Task Representations

Abstract: Multi-task multi-agent reinforcement learning (MT-MARL) is capable of leveraging useful knowledge across multiple related tasks to improve performance on any single task. While recent ...
Morehead State University

MSU student-teachers achieve record edTPA scores

Morehead State University's Ernst & Sara Lane Volgenau College of Education is celebrating a historic academic milestone after student-teachers achieved the highest Educative Teacher Performance ...