Search Results for author: Haoli Bai

Found 17 papers, 7 papers with code

Visually Guided Generative Text-Layout Pre-training for Document Intelligence

1 code implementation25 Mar 2024 Zhiming Mao, Haoli Bai, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu, Kam-Fai Wong

Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and reason both document texts and layouts (e. g., locations of texts and table-cells).

Document Classification document understanding +2

MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric

no code implementations12 Mar 2024 Haokun Lin, Haoli Bai, Zhili Liu, Lu Hou, Muyi Sun, Linqi Song, Ying WEI, Zhenan Sun

We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance.

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding

no code implementations19 Dec 2022 Haoli Bai, Zhiguang Liu, Xiaojun Meng, Wentao Li, Shuang Liu, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu

While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far.

Contrastive Learning document understanding +2

Dynamically pruning segformer for efficient semantic segmentation

no code implementations18 Nov 2021 Haoli Bai, Hongda Mao, Dinesh Nair

In this paper, we seek to design a lightweight SegFormer for efficient semantic segmentation.

Knowledge Distillation Segmentation +1

Towards Efficient Post-training Quantization of Pre-trained Language Models

no code implementations30 Sep 2021 Haoli Bai, Lu Hou, Lifeng Shang, Xin Jiang, Irwin King, Michael R. Lyu

Experiments on GLUE and SQuAD benchmarks show that our proposed PTQ solution not only performs close to QAT, but also enjoys significant reductions in training time, memory overhead, and data consumption.

Quantization

Discrete Auto-regressive Variational Attention Models for Text Modeling

1 code implementation16 Jun 2021 Xianghong Fang, Haoli Bai, Jian Li, Zenglin Xu, Michael Lyu, Irwin King

We further design discrete latent space for the variational attention and mathematically show that our model is free from posterior collapse.

Language Modelling

Discrete Variational Attention Models for Language Generation

no code implementations21 Apr 2020 Xianghong Fang, Haoli Bai, Zenglin Xu, Michael Lyu, Irwin King

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

Language Modelling Text Generation

Efficient Bitwidth Search for Practical Mixed Precision Neural Network

no code implementations17 Mar 2020 Yuhang Li, Wei Wang, Haoli Bai, Ruihao Gong, Xin Dong, Fengwei Yu

Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks.

Quantization

RTN: Reparameterized Ternary Network

no code implementations4 Dec 2019 Yuhang Li, Xin Dong, Sai Qian Zhang, Haoli Bai, Yuanpeng Chen, Wei Wang

We first bring up three omitted issues in extremely low-bit networks: the squashing range of quantized values; the gradient vanishing during backpropagation and the unexploited hardware acceleration of ternary networks.

Quantization

Few Shot Network Compression via Cross Distillation

1 code implementation21 Nov 2019 Haoli Bai, Jiaxiang Wu, Irwin King, Michael Lyu

The core challenge of few shot network compression lies in high estimation errors from the original network during inference, since the compressed network can easily over-fits on the few training instances.

Knowledge Distillation Model Compression

DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification

no code implementations30 Dec 2018 Xianghong Fang, Haoli Bai, Ziyi Guo, Bin Shen, Steven Hoi, Zenglin Xu

In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks.

Classification General Classification +2

PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks

1 code implementation NIPS Workshop CDNNRIA 2018 Jiaxiang Wu, Yao Zhang, Haoli Bai, Huasong Zhong, Jinlong Hou, Wei Liu, Wenbing Huang, Junzhou Huang

Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.

Model Compression

Stochastic Sequential Neural Networks with Structured Inference

no code implementations24 May 2017 Hao Liu, Haoli Bai, Lirong He, Zenglin Xu

Inheriting these advantages of stochastic neural sequential models, we propose a structured and stochastic sequential neural network, which models both the long-term dependencies via recurrent neural networks and the uncertainty in the segmentation and labels via discrete random variables.

Medical Diagnosis Segmentation +2

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