Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed
itself as the trending approach to automate the discovery of deep neural network …

Abstract Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …

Vision transformers (ViTs) inherited the success of NLP but their structures have not been
sufficiently investigated and optimized for visual tasks. One of the simplest solutions is to …

Neural Architecture Search (NAS) is widely used to automatically obtain the neural network
with the best performance among a large number of candidate architectures. To reduce the …

Previous multi-task dense prediction studies developed complex pipelines such as multi-
modal distillations in multiple stages or searching for task relational contexts for each task …

In this paper, we propose a Shapley value based method to evaluate operation contribution
(Shapley-NAS) for neural architecture search. Differentiable architecture search (DARTS) …

One-shot neural architecture search (NAS) methods significantly reduce the search cost by
considering the whole search space as one network, which only needs to be trained once …

Abstract Deep Neural Networks have received considerable attention in recent years. As the
complexity of network architecture increases in relation to the task complexity, it becomes …

In one-shot weight sharing for NAS, the weights of each operation (at each layer) are
supposed to be identical for all architectures (paths) in the supernet. However, this rules out …