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20172022
most citedBinaryBERT: Pushing the Limit of BERT Quantization

45 citations · 114 across the 13 of their papers we have counts for

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7 papers · 1 filter

cs.LG2021

Discrete Auto-regressive Variational Attention Models for Text Modeling

Xianghong Fang, Haoli Bai, Jian Li +3

Variational autoencoders (VAEs) have been widely applied for text modeling. In practice, however, they are troubled by two challenges: information underrepresentation and posterior…

cs.LG2020

Discrete Variational Attention Models for Language Generation

Xianghong Fang, Haoli Bai, Zenglin Xu +2

Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior col…

cs.LG2020★ 13 cited

Efficient Bitwidth Search for Practical Mixed Precision Neural Network

Yuhang Li, Wei Wang, Haoli Bai +3

Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks. Recent efforts propose to quantize weights and activati…

cs.LG2019★ 2 cited

RTN: Reparameterized Ternary Network

Yuhang Li, Xin Dong, Sai Qian Zhang +3

To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed…

cs.LG2019★ 17 cited

Few Shot Network Compression via Cross Distillation

Haoli Bai, Jiaxiang Wu, Irwin King +1

Model compression has been widely adopted to obtain light-weighted deep neural networks. Most prevalent methods, however, require fine-tuning with sufficient training data to ensur…

cs.LG2019

Structured Pruning of Recurrent Neural Networks through Neuron Selection

Liangjian Wen, Xuanyang Zhang, Haoli Bai +1

Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it d…

cs.LG2017★ 3 cited

Stochastic Sequential Neural Networks with Structured Inference

Hao Liu, Haoli Bai, Lirong He +1

Unsupervised structure learning in high-dimensional time series data has attracted a lot of research interests. For example, segmenting and labelling high dimensional time series c…