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OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
OpenELM, a state-of-the-art open language model, prioritizes reproducibility and transparency in large language models. It employs a layer-wise scaling strategy to efficiently allocate parameters within each layer, resulting in enhanced accuracy. For instance, with a parameter budget of one billion, OpenELM shows a 2.36% accuracy improvement compared to OLMo.
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Meet CopilotKit: An Open-Source Copilot Platform for Seamless AI Integration in Any Application
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Top Power BI Books to Read in 2024
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VDTuner: A Machine Learning-Based Automatic Performance Tuning Framework for Vector Data Management Systems (VDMSs)
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Interpretable Deep Learning for Biodiversity Monitoring: Introducing AudioProtoPNet
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An Overview of Advancements in Deep Reinforcement Learning (Deep RL)
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Apple Vision Pro: Use Cases and Special Application in the Biomedical Sector
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KDk: A Novel Machine Learning Framework that Protects Vertical Federated Learning from All the Known Types of Label Inference Attacks with Very High Performance
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Privacy-Preserving Training-as-a-Service (PTaaS): A Novel Service Computing Paradigm that Provides Privacy-Friendly and Customized Machine Learning Model Training for End Devices
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Top 15 AI Libraries/Frameworks for Automatically Red-Teaming Your Generative AI Application