Search Results for author: Xingjian Shi

Found 15 papers, 8 papers with code

Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar

no code implementations13 Apr 2022 Yaojie Hu, Xingjian Shi, Qiang Zhou, Lee Pike

We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code repair method.

Code Repair

Benchmarking Multimodal AutoML for Tabular Data with Text Fields

1 code implementation4 Nov 2021 Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola

We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well.

AutoML

Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing

no code implementations EMNLP (sustainlp) 2021 Haoyu He, Xingjian Shi, Jonas Mueller, Zha Sheng, Mu Li, George Karypis

We aim to identify how different components in the KD pipeline affect the resulting performance and how much the optimal KD pipeline varies across different datasets/tasks, such as the data augmentation policy, the loss function, and the intermediate representation for transferring the knowledge between teacher and student.

Data Augmentation Hyperparameter Optimization

Multimodal AutoML on Structured Tables with Text Fields

2 code implementations ICML Workshop AutoML 2021 Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alex Smola

We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well.

AutoML

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

4 code implementations9 Jul 2019 Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating).

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

no code implementations27 May 2019 Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.

Link Prediction Matrix Completion +1

Machine Learning for Spatiotemporal Sequence Forecasting: A Survey

no code implementations21 Aug 2018 Xingjian Shi, Dit-yan Yeung

Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem.

Trajectory Forecasting

Spatiotemporal Modeling for Crowd Counting in Videos

no code implementations ICCV 2017 Feng Xiong, Xingjian Shi, Dit-yan Yeung

To exploit the otherwise very useful temporal information in video sequences, we propose a variant of a recent deep learning model called convolutional LSTM (ConvLSTM) for crowd counting.

Crowd Counting Frame +1

Dynamic Key-Value Memory Networks for Knowledge Tracing

1 code implementation24 Nov 2016 Jiani Zhang, Xingjian Shi, Irwin King, Dit-yan Yeung

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities.

Knowledge Tracing

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

no code implementations NeurIPS 2016 Hao Wang, Xingjian Shi, Dit-yan Yeung

To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting.

Collaborative Filtering Denoising +1

Natural-Parameter Networks: A Class of Probabilistic Neural Networks

1 code implementation NeurIPS 2016 Hao Wang, Xingjian Shi, Dit-yan Yeung

Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models.

Decision Making Under Uncertainty Link Prediction

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

12 code implementations NeurIPS 2015 Xingjian Shi, Zhourong Chen, Hao Wang, Dit-yan Yeung, Wai-kin Wong, Wang-chun Woo

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Video Prediction Weather Forecasting

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