In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept ...

Microsoft researchers have developed On-Policy Context Distillation (OPCD), a training method that permanently embeds enterprise system prompt instructions into model weights, reducing inference ...

FSU College of Engineering and Florida State University’s Resilient Infrastructure and Disaster Response Center examined several types of flood models to highlight their strengths and weaknesses and ...

MIT researchers introduce a technique that improves how AI systems explain their predictions, helping users assess trust in ...

Japan is an archipelago with diverse climate zones and complex topography that is prone to heavy rain and flooding. Add the growing effects of global warming. These disaster risks are heightened with ...

MIT researchers unveil a new fine-tuning method that lets enterprises consolidate their "model zoos" into a single, continuously learning agent.

MIT introduces Self-Distillation Fine-Tuning to reduce catastrophic forgetting; it uses student-teacher demonstrations and needs 2.5x compute.

Using tumor growth modeling and informed neural networks as early predictive clinical endpoints. 2007 Continuous dispersion for invasive motility. 2009 Invasive growth with cell density and oxygen.