Siqi Sun

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Microsoft ∙ Toyota Technological Institute at Chicago

Cross-Thought for Sentence Encoder Pre-training

In this paper, we propose Cross-Thought, a novel approach to pre-trainin...
10/07/2020 ∙ by Shuohang Wang, et al. ∙ 0 ∙ share

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Contrastive Distillation on Intermediate Representations for Language Model Compression

Existing language model compression methods mostly use a simple L2 loss ...
09/29/2020 ∙ by Siqi Sun, et al. ∙ 0 ∙ share

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Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding

Transformer has become ubiquitous in the deep learning field. One of the...
09/13/2020 ∙ by Shuohang Wang, et al. ∙ 0 ∙ share

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Accelerating Real-Time Question Answering via Question Generation

Existing approaches to real-time question answering (RTQA) rely on learn...
09/10/2020 ∙ by Yuwei Fang, et al. ∙ 0 ∙ share

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FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

Large-scale cross-lingual language models (LM), such as mBERT, Unicoder ...
09/10/2020 ∙ by Yuwei Fang, et al. ∙ 0 ∙ share

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Unsupervised and Supervised Structure Learning for Protein Contact Prediction

Protein contacts provide key information for the understanding of protei...
08/31/2020 ∙ by Siqi Sun, et al. ∙ 0 ∙ share

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Hierarchical Graph Network for Multi-hop Question Answering

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop...
11/09/2019 ∙ by Yuwei Fang, et al. ∙ 0 ∙ share

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DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

We present a large, tunable neural conversational response generation mo...
11/01/2019 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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FreeLB: Enhanced Adversarial Training for Language Understanding

Adversarial training, which minimizes the maximal risk for label-preserv...
09/25/2019 ∙ by Chen Zhu, et al. ∙ 0 ∙ share

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Patient Knowledge Distillation for BERT Model Compression

Pre-trained language models such as BERT have proven to be highly effect...
08/25/2019 ∙ by Siqi Sun, et al. ∙ 0 ∙ share

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Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, ...
09/02/2016 ∙ by Sheng Wang, et al. ∙ 0 ∙ share

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Graphical Model Sketch

Structured high-cardinality data arises in many domains, and poses a maj...
02/09/2016 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling

Deep Convolutional Neural Networks (DCNN) has shown excellent performanc...
11/17/2015 ∙ by Sheng Wang, et al. ∙ 0 ∙ share

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Learning Nonparametric Forest Graphical Models with Prior Information

We present a framework for incorporating prior information into nonparam...
11/12/2015 ∙ by Yuancheng Zhu, et al. ∙ 0 ∙ share

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Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior

Learning the network structure underlying data is an important problem i...
03/07/2015 ∙ by Qingming Tang, et al. ∙ 0 ∙ share

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Siqi Sun

Featured Co-authors

Jianfeng Gao

Yu Cheng

Zhe Gan

Jing Jiang

Zhen Li

Jingjing Liu

Yizhe Zhang

Mohammad Ghavamzadeh

Zhe Liu

Branislav Kveton

Xiang Gao

Cross-Thought for Sentence Encoder Pre-training

Contrastive Distillation on Intermediate Representations for Language Model Compression

Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding

Accelerating Real-Time Question Answering via Question Generation

FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

Unsupervised and Supervised Structure Learning for Protein Contact Prediction

Hierarchical Graph Network for Multi-hop Question Answering

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

FreeLB: Enhanced Adversarial Training for Language Understanding

Patient Knowledge Distillation for BERT Model Compression

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Graphical Model Sketch

AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling

Learning Nonparametric Forest Graphical Models with Prior Information

Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior