IEEE

Meta Learning Task Representation in Multiagent Reinforcement Learning: From Global Inference to Local Inference

Abstract: Multiagent meta reinforcement learning (MAMRL) enables multiagent systems (MASs) to adapt to multiple tasks. However, partial observability poses a significant challenge by hindering ...
EurekAlert!

Explanation-based retrieval boosts grammatical error correction

Grammatical error correction (GEC) is a key task in natural language processing (NLP), widely applied in education, news, and ...
EurekAlert!

Insilico Medicine launches science MMAI gym to train frontier LLMs into pharmaceutical-grade scientific engines

New “AI GYM for Science” dramatically boosts the biological and chemical intelligence of any causal or frontier LLM, ...
Yale Insights

Most Startups Fail. These Founders Thought Making an Impact Was Worth the Risk.

We talked with Nick Callegari ’25, Dianna Liu ’18, and Ariana Yuen ’19 about the unique challenges faced by founders trying to make a difference and the moments that make their work worthwhile.
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 ...