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Found 12 papers, 5 papers with code
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance.
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giving precise model predictions.
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction
Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples.
Meta Gradient Boosting Neural Networks
A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users.
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance.
Opinion Fraud Detection via Neural Autoencoder Decision Forest
Online reviews play an important role in influencing buyers' daily purchase decisions.
NeuRec: On Nonlinear Transformation for Personalized Ranking
Modeling user-item interaction patterns is an important task for personalized recommendations.
