Empiricism without magic: Transformational abstraction in deep convolutional neural networks
C Buckner - Synthese, 2018 - Springer
In artificial intelligence, recent research has demonstrated the remarkable potential of Deep
Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art …
Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art …
Expanding the notion of mechanism to further understanding of biopsychosocial disorders? Depression and medically-unexplained pain as cases in point
JP Konsman - Studies in History and Philosophy of Science, 2024 - Elsevier
Abstract Evidence-Based Medicine has little consideration for mechanisms and
philosophers of science and medicine have recently made pleas to increase the place of …
philosophers of science and medicine have recently made pleas to increase the place of …
LTP Revisited: Reconsidering the Explanatory Power of Synaptic Efficacy
J Najenson - Review of Philosophy and Psychology, 2023 - Springer
Abstract Changes in synaptic strength are described as a unifying hypothesis for memory
formation and storage, leading philosophers to consider the 'synaptic efficacy hypothesis' as …
formation and storage, leading philosophers to consider the 'synaptic efficacy hypothesis' as …
Exploring, expounding & ersatzing: a three-level account of deep learning models in cognitive neuroscience
V Subotić - Synthese, 2024 - Springer
Deep learning (DL) is a statistical technique for pattern classification through which AI
researchers train artificial neural networks containing multiple layers that process massive …
researchers train artificial neural networks containing multiple layers that process massive …