8 citations · 21 across the 6 of their papers we have counts for
11 papers
i-Code: An Integrative and Composable Multimodal Learning Framework
Ziyi Yang, Yuwei Fang, Chenguang Zhu +17
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to…
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition
Kenichi Kumatani, Robert Gmyr, Felipe Cruz Salinas +5
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity. In this work, we investigate how multi-lingual Automatic Speech R…
Dynamic Gradient Aggregation for Federated Domain Adaptation
Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr +2
In this paper, a new learning algorithm for Federated Learning (FL) is introduced. The proposed scheme is based on a weighted gradient aggregation using two-step optimization to of…
Federated Transfer Learning with Dynamic Gradient Aggregation
Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr +2
In this paper, a Federated Learning (FL) simulation platform is introduced. The target scenario is Acoustic Model training based on this platform. To our knowledge, this is the fir…
Sleeping is Efficient: MIS in -rounds Node-averaged Awake Complexity
Soumyottam Chatterjee, Robert Gmyr, Gopal Pandurangan
Maximal Independent Set (MIS) is one of the fundamental problems in distributed computing. The round (time) complexity of distributed MIS has traditionally focused on the \emph{wor…
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Ziyi Yang, Chenguang Zhu, Robert Gmyr +3
Text summarization aims to extract essential information from a piece of text and transform the text into a concise version. Existing unsupervised abstractive summarization models…