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Although launched less than a year ago, China's first AI large model for the diagnosis, operation and maintenance of industrial equipment has already been widely adopted in the coal, chemical and ...

Large Language Models (LLMs) face significant challenges in optimizing their post-training methods, particularly in balancing Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) approaches.

We propose ContrastLOS, a novel graph-based deep learning model that integrates graph transformer networks with contrastive pre-training to enhance ICU LOS prediction ... whether LOS exceeds 3 or 7 ...

OpenAI, Google and Anthropic have all poured billions into training their frontier large language models. While these models have been very helpful, being the foundational models from which ...

Consecutive performance of models to predict adverse outcomes decreases beyond 48 hours after admission. HealthDay News — For the Preeclampsia Integrated Estimate of RiSk (PIERS) Machine Learning ...

Cybersecurity threats are bound to multiply as large language models are ... understanding the lineage of data used in training and operating the models, and be careful not to implicitly trust ...

On the other hand, instance selection methods demonstrate the capability of training AI models with minimised training sets and negligible performance degradation. Despite the growing interest in both ...

Learn More Very small language models (SLMs) can outperform leading large language models (LLMs ... cylces during inference to improve their performance on various tasks. Leading reasoning ...

More powerful and pervasive large language models are creating a new cybersecurity challenge for companies. The risks posed by LLMs, a form of generative artificial intelligence that communicates ...

The avatar, known as Miss Boo, was supposed to offer styling tips to customers and allow them to view jeans and shirts on three-dimensional models before buying. The concept may sound familiar to ...