11 citations · 12 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 1 cited
Tensor train decompositions on recurrent networks
Alejandro Murua, Ramchalam Ramakrishnan, Xinlin Li +2
Recurrent neural networks (RNN) such as long-short-term memory (LSTM) networks are essential in a multitude of daily live tasks such as speech, language, video, and multimodal lear…
cs.CV2020★ 11 cited
Importance of Data Loading Pipeline in Training Deep Neural Networks
Mahdi Zolnouri, Xinlin Li, Vahid Partovi Nia
Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loading data takes a signifi…
cs.LG2019
Random Bias Initialization Improves Quantized Training
Xinlin Li, Vahid Partovi Nia
Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful full-precision training…