3 citations · 3 across the 5 of their papers we have counts for
5 papers
PithTrain: A Compact and Agent-Native MoE Training System
Ruihang Lai, Hao Kang, Haozhan Tang +6
Mixture-of-Experts (MoE) has become the dominant architecture for frontier language models. To meet this demand, production frameworks have built optimized MoE training stacks over…
Identifying Speaker Information in Feed-Forward Layers of Self-Supervised Speech Transformers
Tzu-Quan Lin, Hsi-Chun Cheng, Hung-yi Lee +1
In recent years, the impact of self-supervised speech Transformers has extended to speaker-related applications. However, little research has explored how these models encode speak…
Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization
Tzu-Quan Lin, Wei-Ping Huang, Hao Tang +1
Fine-tuning speech representation models can enhance performance on specific tasks but often compromises their cross-task generalization ability. This degradation is often caused b…
Property Neurons in Self-Supervised Speech Transformers
Tzu-Quan Lin, Guan-Ting Lin, Hung-yi Lee +1
There have been many studies on analyzing self-supervised speech Transformers, in particular, with layer-wise analysis. It is, however, desirable to have an approach that can pinpo…
DAISY: Data Adaptive Self-Supervised Early Exit for Speech Representation Models
Tzu-Quan Lin, Hung-yi Lee, Hao Tang
Self-supervised speech models have shown to be useful for various tasks, but their large size limits the use in devices with low computing power and memory. In this work, we explor…