Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

The Rajasthan High Court directed regularization of the petitioner engaged by the State on a part-time basis, who continued ...

An important meeting of the committee established to regularize the arms manufacturing business cluster of Darra Adam Khel (DAK) was ...

By transferring temporal knowledge from complex time-series models to a compact model through knowledge distillation and attention mechanisms, the ...

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

Understanding brain—behavior relationships and predicting cognitive and clinical outcomes from neuromarkers are central tasks in neuroscience. Connectome-based Predictive Modeling (CPM) has been ...

According to Andrej Karpathy (@karpathy), maintaining strong regularization is crucial to prevent model degradation when applying Reinforcement Learning from Human Feedback (RLHF) in AI systems ...

According to @akshatgupta57, a major revision to their paper on Lifelong Knowledge Editing highlights that better regularization techniques are essential for maintaining consistent downstream ...

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 ...

Abstract: The cross-channel deblurring problem in color image processing is difficult to solve due to the complex coupling and structural blurring of color pixels. Until now, there are few efficient ...