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Found 13 papers, 5 papers with code
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.
g-SiC6 Monolayer: A New Graphene-like Dirac Cone Material with a High Fermi Velocity
In this work, using first-principles calculations, we have predicted a new Dirac cone material of silicon carbide with the new stoichiometries, named g-SiC6 monolayer, which is composed of sp2 hybridized with a graphene-like structure.
Materials Science
Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations
With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates.
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations
With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking.
Jointly Learning to Recommend and Advertise
Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.
Learning to Structure Long-term Dependence for Sequential Recommendation
To better model the long-term dependence structure, we propose a GatedLongRec solution in this work.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention.
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.
Ranked #35 on
Machine Translation
on WMT2014 English-French
Wide & Deep Learning for Recommender Systems
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
Ranked #2 on
Click-Through Rate Prediction
on Bing News
