In recent years, developers have introduced artificial intelligence (AI) systems that can simulate or reproduce various human abilities, such as recognizing objects in images, answering questions, and more. Yet in contrast with the human mind, which can deteriorate over time, these systems typically retain the same performance or even improve their skills over time.In recent years, developers have introduced artificial intelligence (AI) systems that can simulate or reproduce various human abilities, such as recognizing objects in images, answering questions, and more. Yet in contrast with the human mind, which can deteriorate over time, these systems typically retain the same performance or even improve their skills over time.Computer Sciences[#item_full_content]

Reinforcement learning (RL) is a machine learning technique that trains software by mimicking the trial-and-error learning process of humans. It has demonstrated considerable success in many areas that involve sequential decision-making. However, training RL models with real-world online tests is often undesirable as it can be risky, time-consuming, and importantly, unethical. Thus, using offline datasets that are naturally collected through past operations is becoming increasingly popular for training and evaluating RL and bandit policies.Reinforcement learning (RL) is a machine learning technique that trains software by mimicking the trial-and-error learning process of humans. It has demonstrated considerable success in many areas that involve sequential decision-making. However, training RL models with real-world online tests is often undesirable as it can be risky, time-consuming, and importantly, unethical. Thus, using offline datasets that are naturally collected through past operations is becoming increasingly popular for training and evaluating RL and bandit policies.Machine learning & AI[#item_full_content]

In a recent publication in Science China Life Sciences, a research team led by Professor Jing-Dong Jackie Han and Ph.D. student Xinyu Yang from Peking University established a deep learning model for age estimation using non-registered 3D face point clouds. They also proposed the coordinate-wise monotonic transformation algorithm to isolate age-related facial features from identifiable human faces.In a recent publication in Science China Life Sciences, a research team led by Professor Jing-Dong Jackie Han and Ph.D. student Xinyu Yang from Peking University established a deep learning model for age estimation using non-registered 3D face point clouds. They also proposed the coordinate-wise monotonic transformation algorithm to isolate age-related facial features from identifiable human faces.[#item_full_content]

In a recent publication in Science China Life Sciences, a research team led by Professor Jing-Dong Jackie Han and Ph.D. student Xinyu Yang from Peking University established a deep learning model for age estimation using non-registered 3D face point clouds. They also proposed the coordinate-wise monotonic transformation algorithm to isolate age-related facial features from identifiable human faces.In a recent publication in Science China Life Sciences, a research team led by Professor Jing-Dong Jackie Han and Ph.D. student Xinyu Yang from Peking University established a deep learning model for age estimation using non-registered 3D face point clouds. They also proposed the coordinate-wise monotonic transformation algorithm to isolate age-related facial features from identifiable human faces.Computer Sciences[#item_full_content]

We use computers to help us make (hopefully) unbiased decisions. The problem is that machine-learning algorithms do not always make fair classifications if human bias is embedded in the data used to train them—which is often the case in practice.We use computers to help us make (hopefully) unbiased decisions. The problem is that machine-learning algorithms do not always make fair classifications if human bias is embedded in the data used to train them—which is often the case in practice.[#item_full_content]

We use computers to help us make (hopefully) unbiased decisions. The problem is that machine-learning algorithms do not always make fair classifications if human bias is embedded in the data used to train them—which is often the case in practice.We use computers to help us make (hopefully) unbiased decisions. The problem is that machine-learning algorithms do not always make fair classifications if human bias is embedded in the data used to train them—which is often the case in practice.Computer Sciences[#item_full_content]

While the class imbalance issue has been extensively investigated within the multi-class paradigm, its study in the multi-dimensional classification (MDC) context has been limited due to the imbalance shift phenomenon. A sample’s classification as a minor or major class instance becomes ambiguous when it belongs to a minor class in one labeling dimension (LD) and a major class in another.While the class imbalance issue has been extensively investigated within the multi-class paradigm, its study in the multi-dimensional classification (MDC) context has been limited due to the imbalance shift phenomenon. A sample’s classification as a minor or major class instance becomes ambiguous when it belongs to a minor class in one labeling dimension (LD) and a major class in another.[#item_full_content]

The rise of commercially viable generative artificial intelligence (AI) has the potential to transform a vast range of sectors. This transformation will be particularly profound in contemporary military education.The rise of commercially viable generative artificial intelligence (AI) has the potential to transform a vast range of sectors. This transformation will be particularly profound in contemporary military education.Machine learning & AI[#item_full_content]

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