Suche
Ergebnisse
-
Pruning-Aware Merging for Efficient Multitask Inference
(2021)Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD '21)Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost. Pruning each network separately yields suboptimal computation cost due to task relatedness. A promising remedy is to merge the networks ...Conference Paper -
Rethinking Pruning for Accelerating Deep Inference At the Edge
(2020)KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningThere is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses ...Conference Paper -
Pruning Meta-Trained Networks for On-Device Adaptation
(2021)Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementAdapting neural networks to unseen tasks with few training samples on resource-constrained devices benefits various Internet-of-Things applications. Such neural networks should learn the new tasks in few shots and be compact in size. Meta-learning enables few-shot learning, yet the meta-trained networks can be over-parameterised. However, naive combination of standard compression techniques like network pruning with meta-learning jeopardises ...Conference Paper -
p-Meta: Towards On-device Deep Model Adaptation
(2022)KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningConference Paper