4 citations · 7 across the 4 of their papers we have counts for
4 papers
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models
Yuan Shangguan, Haichuan Yang, Danni Li +11
Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model's hyperparameters…
Prompting Large Language Models with Speech Recognition Abilities
Yassir Fathullah, Chunyang Wu, Egor Lakomkin +9
Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. I…
Multi-Head State Space Model for Speech Recognition
Yassir Fathullah, Chunyang Wu, Yuan Shangguan +8
State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches. I…
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Rongzhe Wei, Haoteng Yin, Junteng Jia +2
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured dat…