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Found 15 papers, 8 papers with code
Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar
We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code repair method.
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
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.
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing
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.
Multimodal AutoML on Structured Tables with Text Fields
We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well.
Characterize and Transfer Attention in Graph Neural Networks
Does attention matter and, if so, when and how?
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
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
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.
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
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.
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Spatiotemporal Modeling for Crowd Counting in Videos
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.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
To address these problems, we propose both a new model and a benchmark for precipitation nowcasting.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Dynamic Key-Value Memory Networks for Knowledge Tracing
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.
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
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.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
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.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
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)
