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 ...
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 Learning in Finance

Supervised learning relies on historical, labeled data to train algorithms for specific outcomes. This approach is widely used in the financial sector, where reliable past data can guide future ...
Frontiers

A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer

The growing application of immune checkpoint inhibitors (ICIs) in cancer immunotherapy has underscored the critical need for reliable methods to identify patient populations likely to respond to ICI ...
marktechpost

Memorization vs. Generalization: How Supervised Fine-Tuning SFT and Reinforcement Learning RL Shape Foundation Model Learning

Prior work suggests SFT risks overfitting to training data, making models brittle when faced with new task variants. For example, an SFT-tuned model might excel at arithmetic problems using specific ...