We propose adaptive weight decay, which automatically tunes the hyper-parameter for weight decay during each training iteration. For classification problems, we propose changing the value of the weight decay hyper-parameter on the fly based on the strength of updates from the classification loss (i.e., gradient… Read More »Adaptive Weight Decay Apple Machine Learning Research
*=Equal Contributors Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in computer vision. In this… Read More »4M: Massively Multimodal Masked Modeling Apple Machine Learning Research
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Large language models’ inability to attribute their claims to external knowledge and their tendency to hallucinate makes it difficult to trust their responses. Even humans are prone to factual errors in their writing. Therefore verifying the factual accuracy of textual information, whether generated by large… Read More »FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge Apple Machine Learning Research
We believe generative AI has the potential over time to transform virtually every customer experience we know. The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal… Read More »Welcome to a New Era of Building in the Cloud with Generative AI on AWS Swami Sivasubramanian AWS Machine Learning Blog
Researchers at Korea University have developed a new speech synthesizer called HierSpeech++. This research aims to create synthetic speech that is robust, expressive, natural, and human-like. The team aimed to achieve this without relying on a text-speech paired dataset and to improve existing models’… Read More »Researchers from Korea University Unveil HierSpeech++: A Groundbreaking AI Approach for High-Fidelity, Efficient Text-to-Speech and Voice Conversion Sana Hassan Artificial Intelligence Category – MarkTechPost
In the rapidly advancing fields of Artificial Intelligence (AI) and Machine Learning (ML), finding effective, automated, and adaptable approaches has become significantly crucial. The constant upliftment of AI and ML approaches has reshaped the possibilities of what machines can accomplish and how humans interact… Read More »Meet Relational Deep Learning Benchmark (RelBench): A Collection of Realistic, Large-Scale, and Diverse Benchmark Datasets for Machine Learning on Relational Databases Tanya Malhotra Artificial Intelligence Category – MarkTechPost
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models… Read More »Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio Raghu Ramesha AWS Machine Learning Blog
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service.… Read More »Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements Melanie Li AWS Machine Learning Blog
Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular… Read More »New – Code Editor, based on Code-OSS VS Code Open Source now available in Amazon SageMaker Studio Eric Peña AWS Machine Learning Blog