Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Linear algebra is essential for understanding core data science concepts like machine learning, neural networks, and data ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Cracking the code to becoming an AI genius isn't about shortcuts—it’s a marathon of mathematical rigor, deep learning mastery, and relentless research ...

Hence, in this paper, we introduce Modality Linear Representation-Steering (MoReS) to achieve the goal. MoReS effectively re-balances the intrinsic modalities throughout the model, where the key idea ...

There is a need for design strategies that can support rapid and widespread deployment of new energy systems and process technologies. In a previous work, we introduced process family design as an ...

We conclude that sequence action representations contextually differentiate during early skill learning, an issue relevant to brain-computer interface applications in neurorehabilitation. Introduction ...

In the rapidly evolving landscape of machine learning and artificial intelligence, understanding the fundamental representations within transformer models has emerged as a critical research challenge.

Limited annotated data available for the recognition of facial expression and particularly action units makes it hard to train a deep network which can learn disentangled invariant features. However, ...

Now, according to the linear representation hypothesis, in neural networks, concepts are encoded within the linear subspaces of representations. Recent models have found this claim to be true in ...