Deep Neural Networks (DNNs) have proven to be extremely effective at learning a wide range of tasks. Due to their complexity and inexplicable internal state, DNNs are difficult to analyze. Several attempts to interpret their operation have been made, but analyzing them from the perspective of the knowledge encoded in their layers is a promising research direction. The purpose of this survey is two-fold: a) to review the concept of DNN knowledge quantification and highlight it as an important near-future challenge, and b) to provide a brief account of the scant existing methods attempting to actually quantify DNN knowledge.
This work introduces a new architectural component that embeds structured inductive bias into deep learning: an attention mechanism operating over tensor-product representations (TPRs) that outperforms existing architectural components in combinatorial generalization.
Neuroscientific learning theories propose that the brain acquires knowledge by constructing internal world models. Supervised learning, the dominant approach in deep neural networks (DNN), relies on external category labels, making it difficult to reconcile with biological learning. There is an increasing trend towards...
Dirk Gütlin, Denise Kittelmann, Ryszard Auksztulewicz· bioRxiv· 0 citations
Stochastic neural network broadens deep learning from prediction‐oriented function approximation to a richer framework for statistical inference, and two related architectures are discussed: kernel‐expanded StoNet and sublinearly structured DNNs, which can be viewed as structural or theoretical extensions of the StoNet...
Artificial Intelligence (AI) refers to the broad field where machines are trained to carry out tasks such as learning, reasoning and decision-making. Deep Learning (DL) is a branch of AI that relies on multi-layered neural networks to extract features automatically, handling complex data like images and videos with hig...
S. Sri, S. Lakshmi, R. S. Suresh Kumar· International Journal of Dru...· 0 citations
The field of computer vision has passed through a paradigm shift with the appearance of Vision Transformers (ViTs), an revolutionary technique inspired by natural language processing (NLP). ViTs represents a paradigm shift, transferring far from traditional convolutional neural networks (CNNs) by the usage of the stren...
Abdelhafid Berroukham, Mohammed Lahraichi· Advances in Neutrosophic Mat...· 0 citations
This paper proposes a novel regularization technique designed to enhance the robustness of deep-learning-based knowledge tracing models, while simultaneously improving their interpretability.
S. Girard, Juan D. Pinto, Jill-Jênn Vie et al.· 0 citations
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