Solving brain dynamics gives rise to flexible machine-learning models
Last year, MIT researchers announced that they had built “liquid” neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job and can adapt to changing conditions, for real-world safety-critical tasks, like driving and flying. The flexibility of these “liquid” neural nets meant boosting the bloodline to our connected world, yielding better decision-making for many tasks involving time-series data, such as brain and heart monitoring, weather forecasting, and stock pricing.
Artificial neural networks, ubiquitous machine-learning models that can be trained to complete many tasks, are so called because their architecture is inspired by the way biological neurons process information in the human brain.
Like a collection of 'Pick Up Sticks', this neural network has passed a critical step for developing machine intelligence. For the first time, a physical neural network has successfully been shown to learn and remember 'on the fly', in a way inspired by and similar to how the brain's neurons…
Neural networks, a type of machine-learning model, are being used to help humans complete a wide variety of tasks, from predicting if someone's credit score is high enough to qualify for a loan to diagnosing whether a patient has a certain disease. But researchers still have only a limited understanding…
Model weights: https://huggingface.co/Comfy-Org/Lens PR: https://github.com/Comfy-Org/ComfyUI/pull/14077 You'll need to git the merge pull request if you're…