Sep 18, 2023 · Therefore, using knowledge graphs and deep learning algorithms can make cultural heritage management more intelligent, address fragmentary cultural data, and enhance visualization...

In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used.

Jan 20, 2022 · In semi-deep infusion, external knowledge is involved through attention mechanisms or learnable knowledge constraints acting as a sentinel to guide model learning. Deep infusion...

Dec 24, 2019 · In this paper, we review the main XAI approaches existing in the literature, underlying their strengths and limitations, and we propose neural-symbolic integration as a cornerstone to design an AI which is closer to non-insiders comprehension.

interpretable deep learning algorithms. Real-world domain knowledge is rich. In the context of deep learning, the domain knowledge mainly originates from two sources: target knowledge and measurement knowledge. Target knowledge governs the behaviors and properties of the target variables we intend to predict, while measurement

Mar 18, 2025 · In practice, this means KBLaM can store and process over 10,000 knowledge triples, the equivalent of approximately 200,000 text tokens on a single GPU—a feat that would be computationally prohibitive with conventional in-context learning. The results across a wide range of triples and can be seen in Figure 3.

Nov 30, 2022 · To better mimic the behavior of human brains, different advanced methods have been proposed to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning, which we refer to as knowledge-augmented deep learning (KADL).

Jan 1, 2024 · To evaluate the efficacy and efficiency of our proposed model, we utilize deep learning algorithms like RNN, GRU, LSTM, GPT-3 and BERT Transformer providing an acceptable level of accuracy.

To better mimic the behavior of human brains, different advanced methods have been proposed to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning, which we refer to as …

Sep 29, 2023 · Knowledge distillation refers to the process of transferring the knowledge from a large unwieldy model or set of models to a single smaller model that can be practically deployed under real-world constraints. Essentially, it is a form of model compression that was first successfully demonstrated by Bucilua and collaborators in 2006 [2].