We demonstrate that the lost information after factorizing one layer is small and can be rapidly retrieved; hence, sequential factorization is more efficient.

Mar 5, 2017 · In this paper, we address two problems associated with that LRF scheme and we propose a compellingly simple methodology to overcome them. First, ...

Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.

Exploiting sequential low-rank factorization for multilingual DNNs. R Sahraeian, D Van Compernolle. 2017 IEEE International Conference on Acoustics, Speech ...

Using Weighted Model Averaging in Distributed Multilingual DNNs to Improve Low Resource ASR · Exploiting sequential Low-Rank Factorization for multilingual DNNS.

Exploiting sequential low-rank factorization for multilingual DNNS. ... A study of rank-constrained multilingual DNNS for low-resource ASR. Proceedings ...

Denseflex: A Low Rank Factorization Methodology for Adaptable Dense Layers in DNNs ... Exploiting sequential Low-Rank Factorization for multilingual DNNS.

A low-rank matrix factorization of the final weight layer is proposed and applied to DNNs for both acoustic modeling and language modeling.

Exploiting sequential low-rank factorization for multilingual DNNs. R Sahraeian, D Van Compernolle. 2017 IEEE International Conference on Acoustics, Speech ...

Jun 30, 2024 · In this paper, we exploit the fact that LoRA only performs low-rank up- dates to the quantized model to derive an initialization scheme that ...