MIT's new fine-tuning method lets LLMs learn new skills without losing old ones

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

CEOs Are Learning to Live With Trump’s Turn to State Capitalism

Last week Nvidia finally got permission to sell one of its most advanced semiconductor chips to China. The catch: The federal government will take 25% of the revenue from those sales. The Nvidia deal ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent Eligible Machine-Learning Claims

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
IEEE

Supervised Learning in Dynamic and Non Stationary Environments

Abstract: One central theme in machine learning is function estimation from sparse and noisy data. An example is supervised learning where the elements of the training set are couples, each containing ...
IEEE

Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples

Abstract: Semi-supervised learning (SSL) methods have shown promising results in solving many practical problems when only a few labels are available. The existing methods assume that the class ...
Hosted on MSN

Logistic Regression Machine Learning Example ¦ Simply Explained

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...
VentureBeat

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Two popular approaches for customizing ...
Scientific Research Publishing

Belkin, M., Niyogi, P. and Sindhwani, V. (2006) Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...
List of Banks in Belarus

Supervised vs. Unsupervised AI in Finance: Where Machine Learning is Heading

An image depicting the integration of AI technologies in banking, showcasing how legacy banks can evolve with AI advancements for improved customer experiences and operational efficiency. Supervised ...