HASSAN, R. Network Time and the New Knowledge Epoch. Time & Society, v. 12, n. 2/3, p. 225-41, 2003.Hassan, R. (2003). Network Time and the new knowledge epoch. Time & Society, 12(2/3), 225-241.Hassan, R. (2003). Network time and the new knowledge epoch. Brit...
This is due to the nature of NNs, also known as the ”black-box” issue: instead of solving the tasks with logical steps, NNs learn by examples and adjust parameters to improve their performance over time26. Recent progress has been made to interpret how NNs solve scientific problems. In ...
The goal here is to extract knowledge from the scientific literature that can subsequently be processed by computer algorithms. At first glance, a natural first step would be to use large language model (such as GPT36, Gopher7, MegaTron8or PaLM9) on each article to extract concepts and their...
Also, uiℓ is called propagation function and calculates the total input value at a given time, known as neuron's state, by adding all the m individual inputs oℓ−1 it receives after first multiplying them with their corresponding weights Wiℓ and adding the corresponding bias biℓ...
which reflect many of the basic features of the human brain, such asadaptability, self-organization and strong learning ability. In ANN, a collection of ‘neurons’ was constructed based on previously solved knowledge to makes new decisions, classifications and forecasts (Blattberg et al., 200...
Note that we adopted the early-stopping strategy to determine the convergence epochs, with epoch numbers of 35 and 40 for the IMDb and Amazon datasets, respectively. In addition, we recorded the data partition for each running time and used the same data partition on all benchmark methods. ...
Large-scale real-world GNN models: We focus on the need of GNN applications in challenging real-world scenarios, and support learning on diverse types of graphs, including but not limited to: scalable GNNs for graphs with millions of nodes; dynamic GNNs for node predictions over time; heterogen...
After the output is generated, the network compares it to the correct output (thetarget) and calculates theerror. Backpropagationis the process of sending the error back through the network to adjust the weights of the connections between neurons. The goal is to reduce this error over time, ...
The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can ...
Recently, brain-inspired computing models have shown great potential to outperform today’s deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown