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The use of explainable artificial intelligence (XAI) in pain research, specifically in understanding the role of electrodermal activity for automated pain recognition: hand-crafted features and deep learning models in pain recognition, highlighting the insights that simple hand-crafted features can yield comparative performances to deep ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).
Higher order message passing is a deep learning model defined on a topological domain and relies on message passing information among entities in the underlying domain in order to perform a learning task. [1] Let be a topological domain.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Models have shown examples of shortcut learning, which is when a system makes unrelated correlations within data instead of using human-like understanding. [27] One such experiment conducted in 2019 tested Google’s BERT LLM using the argument reasoning comprehension task. BERT was prompted to choose between 2 statements, and find the one most ...
It is named "chinchilla" because it is a further development over a previous model family named Gopher. Both model families were trained in order to investigate the scaling laws of large language models. [2] It claimed to outperform GPT-3. It considerably simplifies downstream utilization because it requires much less computer power for ...
Random forests are a way of averaging multiple deep decision trees, trained on different parts of the same training set, with the goal of reducing the variance. [3]: 587–588 This comes at the expense of a small increase in the bias and some loss of interpretability, but generally greatly boosts the performance in the final model.
The current (third) wave has been marked by advances in deep learning, which have made possible the creation of large language models. [3] The success of deep-learning networks in the past decade has greatly increased the popularity of this approach, but the complexity and scale of such networks has brought with them increased interpretability ...