30 citations · 45 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in…
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series. Conventional SDRE algorithms can fail to esti…
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high. One algorithm that achieves this…
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation
Makoto Takamoto, Yusuke Morishita, Hitoshi Imaoka
Compressing deep neural network (DNN) models becomes a very important and necessary technique for real-world applications, such as deploying those models on mobile devices. Knowled…