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- Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform243
The mRMR feature selection methods for classification problem in a marketing machine learning platform at Uber that automates creation and deployment of targeting and personalization models at scale are extended by introducing a non-linear feature redundancy measure and a model-based feature relevance measure.
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- Microdisk modulator-assisted optical nonlinear activation functions for photonic neural networks16
A convolutional neural network is studied to perform handwritten digit classification task, and an accuracy as large as 98% is demonstrated, which verifies the effectiveness of the use of the high-speed microdisk modulator to realize multiple NAFs.
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- DFDS: A Domain-Independent Framework for Document-Level Sentiment Analysis Based on RST12
A domain-independent framework for document-level sentiment classification with weighting rules based on Rhetorical Structure Theory is proposed, which has better performance on datasets in different domains, compared with state-of-art document- level sentiment analysis systems based onRST.
- Safely and Quickly Deploying New Features with a Staged Rollout Framework Using Sequential Test and Adaptive Experimental Design11
This paper proposes a methodology for rolling out features in an automated way using an adaptive experimental design and presents one monitoring algorithm and three ramping up algorithms including time-based, power- based, and risk-based (a Bayesian approach) schedules.
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- Top-N-Targets-Balanced Recommendation Based on Attentional Sequence-to-Sequence Learning10
A Top-N-targets-balanced recommendation based on attentional sequence-to-sequence (Seq2Seq) learning to capture the users’ transient interests and modify the recommendation list generation method to further improve the performance.
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- A Memory-Efficient Approach to the Scalability of Recommender System With Hit Improvement6
This paper proposes a recommender system based on the memory-efficient recurrent neural network that can clearly improve the performance of recommendation, such as hit rate and normalized discounted cumulative gain, when compared to the state-of-the-art recommender algorithm.
- A Top-N-Balanced Sequential Recommendation Based on Recurrent Network5
A top-N-balanced sequential recommendation based on recurrent neural network to solve the low accuracy problem of the recommender system for long term users and balance the top-N recommendation and sequential recommendation to generate a better recommender list by improving the loss function and generation method.
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- Smooth trajectory planning of an unmanned aerial vehicle using an artificial bee colony algorithm4
The UAV trajectory planning was transformed into an optimization problem through modeling, and the optimal solution of the multi-dimensional function was given by taking advantage of the artificial bee colony algorithm.
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- CloudCoT: A Blockchain-Based Cloud Service Dependency Attestation Framework2
A blockchain based cloud service dependency attestation framework–CloudCoT (Cloud Chain-of-Trust) which combines trusted computing and blockchain technology, cloud users are able to automatically extract the valid dependency of their applications deployed on cloud.
- Overview of the NLPCC 2018 Shared Task: Automatic Tagging of Zhihu Questions2
This paper gives an overview for the shared task at the CCF Conference on Natural Language Processing & Chinese Computing (NLPCC 2018): Automatic Tagging of Zhihu Questions, which consists 25551 tags and 721608 training samples in this shared task.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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