There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and …
J Xu, W Zhou, Z Fu, H Zhou, L Li - arXiv preprint arXiv:2111.05193, 2021 - arxiv.org
In recent years, larger and deeper models are springing up and continuously pushing state- of-the-art (SOTA) results across various fields like natural language processing (NLP) and …
Modern neural networks are increasingly bottlenecked by the limited capacity of on-device GPU memory. Prior work explores dropping activations as a strategy to scale to larger neural …
Recent advances in Information Retrieval utilise energy-intensive hardware to produce state- of-the-art results. In areas of research highly related to Information Retrieval, such as Natural …
Y Hu, R Ghosh, R Govindan - Proceedings of the ACM Symposium on …, 2021 - dl.acm.org
Advances in deep learning (DL) have prompted the development of cloud-hosted DL-based media applications that process video and audio streams in real-time. Such applications …
Video cameras are among the most ubiquitous sensors in the Internet-of-Things. Video and audio applications, such as cross-camera activity detection, avatar extraction or language …
As research and practice in artificial intelligence (AI) grow in leaps and bounds, the resources necessary to sustain and support their operations also grow at an increasing …
X Wu, H Xu, Y Wang - Proceedings of the 4th Asia-Pacific Workshop on …, 2020 - dl.acm.org
DNN inference is becoming prevalent for many real-world applications. Current machine learning frameworks usually schedule inference tasks with the goal of optimizing throughput …
Transformer-based models have achieved tremendous success in sequential recommendation (SR), but they suffer from consuming excessive computational resources …