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Found 6 papers, 4 papers with code
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification
Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series.
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series
To resolve this tradeoff, we propose Adaptive Multi-scale Pooling, which aggregates features from an adaptive number of layers, i. e., only the first few layers for short data and more layers for long data.
Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition
Furthermore, comparative experiments with existing state-of-the-art HFR methods show that our method achieves a comparable HFR performance on the Oulu-CASIA NIR&VIS dataset with less degradation of VIS performance.
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible.
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high.
Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices
In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems.
