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Found 12 papers, 1 papers with code
Bias in Generative AI
Moreover, we found that the evident gender and racial biases uncovered in our analysis were even more pronounced than the status quo when compared to labor force statistics or Google images, intensifying the harmful biases we are actively striving to rectify in our society.
Decoupling Degradation and Content Processing for Adverse Weather Image Restoration
The former facilitates channel-dependent degradation removal operation, allowing the network to tailor responses to various adverse weather types; the latter, by integrating Fourier's global properties into channel-independent content features, enhances network capacity for consistent global content reconstruction.
Finite Horizon Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems
In this article, the deep deterministic policy gradient (DDPG) method is used to learn an optimal control policy of a multi-region state-dependent switched system.
Data-Driven Newton Raphson Controller Based on Koopman Operator Theory
To solve these problems, in this work, we utilize the Koopman operator framework to reconstruct a linear model of the original nonlinear dynamical system and then utilize the output of the new linear system as the predictor of the Newton-Raphson controller.
Energy Optimal Control of a Harmonic Oscillator with a State Inequality Constraint
In this article, the optimal control problem for a harmonic oscillator with an inequality constraint is considered.
Deep transfer learning for system identification using long short-term memory neural networks
We apply these techniques to identify two dynamical systems, namely a second-order linear system and a Wiener-Hammerstein nonlinear system.
Motion-Aware Transformer For Occluded Person Re-identification
Recently, occluded person re-identification(Re-ID) remains a challenging task that people are frequently obscured by other people or obstacles, especially in a crowd massing situation.
The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST Dataset
Federated Learning is an algorithm suited for training models on decentralized data, but the requirement of a central "server" node is a bottleneck.
What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education
Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education.
Centroid estimation based on symmetric KL divergence for Multinomial text classification problem
We define a new method to estimate centroid for text classification based on the symmetric KL-divergence between the distribution of words in training documents and their class centroids.
A Surrogate-based Generic Classifier for Chinese TV Series Reviews
With the emerging of various online video platforms like Youtube, Youku and LeTV, online TV series' reviews become more and more important both for viewers and producers.
