Releases a large multi-language code corpus and expert-annotated challenge to benchmark semantic code search.
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AMAD is an end-to-end model using adversarial autoencoders and RNNs with attention for multiscale anomaly detection on time-evolving high-dimensional categorical data.
DSCF is a deep social collaborative filtering model that uses distant neighbors and item-relevant opinions from social networks to improve recommendation accuracy over prior deep models.
citing papers explorer
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CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Releases a large multi-language code corpus and expert-annotated challenge to benchmark semantic code search.
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AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
AMAD is an end-to-end model using adversarial autoencoders and RNNs with attention for multiscale anomaly detection on time-evolving high-dimensional categorical data.
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Deep Social Collaborative Filtering
DSCF is a deep social collaborative filtering model that uses distant neighbors and item-relevant opinions from social networks to improve recommendation accuracy over prior deep models.