Search Results for author:
Found 5 papers, 1 papers with code
Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition
To address this problem, teacher-explorer-student (T/E/S) learning, which adopts the concept of open set recognition (OSR) that aims to reject unknown samples while minimizing the loss of classification performance on known samples, is proposed in this study.
Collective Decision of One-vs-Rest Networks for Open Set Recognition
For this purpose, a novel network structure is proposed, in which multiple one-vs-rest networks (OVRNs) follow a convolutional neural network feature extractor.
Freudian and Newtonian Recurrent Cell for Sequential Recommendation
The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed items in some defined space and grasp the user's long-term and short-term preferences based on the item embeddings.
One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
Furthermore, the network yields a sophisticated nonlinear features-to-output mapping that is explainable in the feature space.
Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission Execution
This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE).
