Due to theself-awarenessof aDCNN, an open-set visual task is not preferred. A DCNN maintains intelligent self-awareness to determine if a task is favorable; the network will accept and execute what it is good at and reject what it cannot do; as such, an open-set visual task will be ...
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains o
It is also important for UEs to be agnostic about the MEC server information. Authors in [93], formulated the task offloading problem as an optimization problem and proposed a heuristic swap matching-based algorithm to solve this problem. Authors in [94] proposed a heuristic algorithm for task...
participants additionally had to have no self-reported visual impairment. The exclusion criteria were preregistered and were as follows. In both studies, we excluded participants who failed the tutorial or did not finish the inspection task on time. Participants with obvious misbehavior...
self-attention architectures like the Transformer (Vaswani et al., 2017). Learning to perform a single task can be expressed in a probabilistic framework as estimating a conditional distri- bution p(output|input) . Since a general system should be able to perform many different tasks, even ...
The basic idea of prompt-tuning is to insert text templates into the input and transform the clas- sification task into a masked language modeling (MLM) problem. While prompt-tuning has shown considerable success in text classification tasks, its application to RE has been limited by the ...
self-attention architectures like the Transformer (Vaswani et al., 2017). Learning to perform a single task can be expressed in a probabilistic framework as estimating a conditional distri- bution p(output|input) . Since a general system should be able to perform many different tasks, even ...
Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. Sydney, 2017. 1126–1135 Mi F, Huang M, Zhang J, et al. Meta-learning for low-resource natural language genera...
While the ISMRM-RD format [53] represents a step towards an open vendor-agnostic format for storing such data, integrating private datasets into open-source toolboxes remains limited. Such limitation is also identified in our work, and further limitations include the fact that ATOMMIC currently ...
We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides the RG