Gradient-based meta-learning techniques are both widely applicable and proficient at
solving challenging few-shot learning and fast adaptation problems. However, they have …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently
identifying the optimal storage temperature requires modeling hydrogen loading as a …

The impressive lifelong learning in animal brains is primarily enabled by plastic changes in
synaptic connectivity. Importantly, these changes are not passive, but are actively controlled …

In this work, we propose a novel meta-learning approach for few-shot classification, which
learns transferable prior knowledge across tasks and directly produces network parameters …

Predicting drug synergy is critical to tailoring feasible drug combination treatment regimens
for cancer patients. However, most of the existing computational methods only focus on data …

The capability of learning and generalizing from very few samples successfully is a
noticeable demarcation separating artificial intelligence and human intelligence. Despite the …

With the rapid development of deep learning methods, the data-driven approach has shown
powerful advantages over the model-driven one. In this paper, we propose an end-to-end …

This paper explores the use of autoencoders in enhancing the management and security of
power systems applications. Research indicates that auto encoders are effective in …

The plastic modifications in synaptic connectivity is primarily from changes triggered by
neuromodulated dopamine signals. These activities are controlled by neuromodulation …