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  • Machine Learning - IBM Research
    Machine learning uses data to teach AI systems to imitate the way that humans learn They can find the signal in the noise of big data, helping businesses improve their operations We’ve been in the field since since the beginning: IBMer Arthur Samuel even coined the term “Machine Learning” back in 1959
  • Quantum Machine Learning: An Interplay Between Quantum Computing and . . .
    Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research This paper presents an overview of quantum computing for the machine learning
  • What are foundation models? - IBM Research
    What makes these new systems foundation models is that they, as the name suggests, can be the foundation for many applications of the AI model Using self-supervised learning and transfer learning, the model can apply information it’s learnt about one situation to another
  • Introducing AI Fairness 360 - IBM Research
    Machine learning models are increasingly used to inform high-stakes decisions about people Although machine learning, by its very nature, is always a form of statistical discrimination, the discrimination becomes objectionable when it places certain privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage Bias in training data, due to either
  • DeepTools: A Full-Stack Machine Learning Compiler for the IBM Spyre . . .
    The rapid growth of machine learning applications has placed unprecedented pressure on conventional computing platforms, exposing the limitations of general-purpose processors in delivering the performance, efficiency, and scalability required by modern AI workloads Addressing these demands has led to a surge in domain-specialized hardware designs and programming paradigms that rethink how
  • Quantum Machine Learning for minimal omics datasets with large feature . . .
    Quantum Machine Learning for minimal omics datasets with large feature space using embeddings and feature selection techniques Abstract Despite the amount of omics data generated in the last decade, the low data regime remains a significant challenge in healthcare, particularly in clinical trials and the study of rare diseases
  • What is retrieval-augmented generation (RAG)? - IBM Research
    RAG is an AI framework for retrieving facts to ground LLMs on the most accurate information and to give users insight into AI’s decision making process
  • Quantum Machine Learning - IBM Research
    We now know that quantum computers have the potential to boost the performance of machine learning systems, and may eventually power efforts in fields from drug discovery to fraud detection We’re doing foundational research in quantum ML to power tomorrow’s smart quantum algorithms
  • Machine Learning for Practical Quantum Error Mitigation
    We benchmark a variety of machine learning models---linear regression, random forests, multi-layer perceptrons, and graph neural networks---on diverse classes of quantum circuits, over increasingly complex device-noise profiles, under interpolation and extrapolation, and for small and large quantum circuits
  • AutoPeptideML 2: An open source library for democratizing machine . . .
    We present AutoPeptideML, an open-source, user-friendly machine learning platform designed to bridge this gap It empowers experimental scientists to build custom predictive models without specialized computational knowledge, enabling active learning workflows that optimize experimental design and reduce sample requirements





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