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

An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

Ruyi Ding (Northeastern University), Tong Zhou (Northeastern University), Lili Su (Northeastern University), Aidong Adam Ding (Northeastern University), Xiaolin Xu (Northeastern University), Yunsi Fei ...

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

What is overfitting and underfitting in machine learning? What is Bias and Variance? Overfitting and Underfitting are two common problems in machine learning and Deep learning. If a model has low ...

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 ...

Abstract: Although most of the patients' recordings includes large scale long-term physiological time series, the patient-level quantity is relatively small, posing great challenges for machine ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

Background: Diabetic retinopathy (DR) screening faces critical challenges in early detection due to its asymptomatic onset and the limitations of conventional prediction models. While existing studies ...

This new transition to more renewable forms of energy is increasing the demand for many critical minerals, which is in turn increasing the interest in the deep sea as a source of these minerals.