48 citations · 71 across the 7 of their papers we have counts for
7 papers
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Yuhang Li, Ruihao Gong, Xu Tan +6
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training…
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
Yichao Zhou, Yu Yan, Rujun Han +5
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a vari…
Long Document Ranking with Query-Directed Sparse Transformer
Jyun-Yu Jiang, Chenyan Xiong, Chia-Jung Lee +1
The computing cost of transformer self-attention often necessitates breaking long documents to fit in pretrained models in document ranking tasks. In this paper, we design Query-Di…
Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition
Ziwen He, Wei Wang, Jing Dong +1
Gait recognition is widely used in social security applications due to its advantages in long-distance human identification. Recently, sequence-based methods have achieved high acc…
Transfer Learning for Sequences via Learning to Collocate
Wanyun Cui, Guangyu Zheng, Zhiqiang Shen +2
Transfer learning aims to solve the data sparsity for a target domain by applying information of the source domain. Given a sequence (e.g. a natural language sentence), the transfe…
Irregular Convolutional Neural Networks
Jiabin Ma, Wei Wang, Liang Wang
Convolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate th…