188 citations · 288 across the 20 of their papers we have counts for
7 papers · 1 filter
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis
Zixuan Yuan, Yada Zhu, Wei Zhang +3
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in…
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent
Wei Zhang, Mingrui Liu, Yu Feng +3
Distributed Deep Learning (DDL) is essential for large-scale Deep Learning (DL) training. Synchronous Stochastic Gradient Descent (SSGD) 1 is the de facto DDL optimization method.…
Asynchronous Decentralized Distributed Training of Acoustic Models
Xiaodong Cui, Wei Zhang, Abdullah Kayi +5
Large-scale distributed training of deep acoustic models plays an important role in today's high-performance automatic speech recognition (ASR). In this paper we investigate a vari…
4-bit Quantization of LSTM-based Speech Recognition Models
Andrea Fasoli, Chia-Yu Chen, Mauricio Serrano +9
We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition…
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation
Wei Zhang, Ziming Huang, Yada Zhu +3
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based…
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen, Jiamin Ni, Songtao Lu +8
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, num…