Search Results for author:
Found 4 papers, 1 papers with code
Accelerating Distributed Deep Learning using Lossless Homomorphic Compression
As deep neural networks (DNNs) grow in complexity and size, the resultant increase in communication overhead during distributed training has become a significant bottleneck, challenging the scalability of distributed training systems.
On a Foundation Model for Operating Systems
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes).
Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction
Text-to-Text Transfer Transformer (T5) has recently been considered for the Grapheme-to-Phoneme (G2P) transduction.
Self-Supervised Visual Representation Learning via Residual Momentum
To solve this problem, we propose "residual momentum" to directly reduce this gap to encourage the student to learn the representation as close to that of the teacher as possible, narrow the performance gap with the teacher, and significantly improve the existing SSL.
