main webpage W Topic Artificial Intelligence arXiv (Cornell University) SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems 2023 Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, thi… Article Open Artificial Intelligence Open Article Page

Artificial Intelligence

Intelligence of machines Artificial intelligence ( AI ) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g. Critical Symbolic Virtual Narrative Open Artificial Intelligence Open Article Page

Exploring foci of: arXiv (Cornell University) SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems 2023 Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synth… Open Article Page

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Explore Artificial Intelligence W Topic Artificial Intelligence arXiv (Cornell University) SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems 2023 Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, thi… Article Critical Symbolic Virtual Narrative Open Artificial Intelligence Open Article Page