We find that by using variant. We show that in situations where there is NCL, by aggressive modality dropout we are able to applying aggressive modality dropout we are able to reverse reverse negative co-learning (NCL) to positive NCL to PCL. While there is prior work documenting the effectiveness co-learning (PCL). Aggressive modality dropout of modality modality dropout during co-learning can be used to'prep' a multimodal model for and multimodal machine learning, we are the first to show unimodal deployment, and dramatically increases that modality dropout can reverse NCL to PCL. model performance during negative co-learning, where during some experiments we saw a 20% gain in accuracy.

Online question-and-answer (Q\&A) systems based on the Large Language Model (LLM) have progressively diverged from recreational to professional use. This paper proposed a Multi-Agent framework with environmentally reinforcement learning (E-RL) for code correction called Code Learning (Co-Learning) community, assisting beginners to correct code errors independently. It evaluates the performance of multiple LLMs from an original dataset with 702 error codes, uses it as a reward or punishment criterion for E-RL; Analyzes input error codes by the current agent; selects the appropriate LLM-based agent to achieve optimal error correction accuracy and reduce correction time. Experiment results showed that 3\% improvement in Precision score and 15\% improvement in time cost as compared with no E-RL method respectively. Our source code is available at: https://github.com/yuqian2003/Co_Learning

Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on resource-constrained devices. We present a novel procedure based on knowledge distillation for compressing an unsupervised anomaly detection model into a supervised deployable one and we suggest a set of techniques to improve the detection sensitivity. Compressed models perform comparably to their larger counterparts while significantly reducing the size and memory footprint.