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​Introduction In machine learning, no single model is perfect. Introduction In machine learning, no single model is perfect.  Read More  

​[[{“value”:” Real-time agents, live dubbing, and simultaneous translation die by a thousand milliseconds. Most “streaming” TTS (Text to Speech) stacks still wait for a chunk of text before they emit sound, so the human hears a beat of silence before the voice starts. VoXtream—released by… Read More »Meet VoXtream: An Open-Sourced Full-Stream Zero-Shot TTS Model for Real-Time Use that Begins Speaking from the First Word Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:”This paper was accepted at the DataWorld (Data Curation) Workshop at ICML 2025. Multimodal models are trained on large-scale web-crawled datasets, which often contain noise, bias, and irrelevant information. This motivates the use of data selection techniques, which can be divided into model-free variants, relying… Read More »Evaluating Sample Utility for Data Selection by Mimicking Model Weights Apple Machine Learning Research

​Local-global attention models have recently emerged as compelling alternatives to standard Transformers, promising improvements in both training and inference efficiency. However, the crucial choice of window size presents a Pareto tradeoff: larger windows maintain performance akin to full attention but offer minimal efficiency gains in… Read More »RATTENTION: Towards the Minimal Sliding Window Size in Local-Global Attention Models Apple Machine Learning Research

​[[{“value”:”Natural language processing (NLP) remains one of the most quickly evolving fields in AI, as new research continues to rapidly advance large language models (LLMs), systems for speech recognition and generation, language agents, and more. This technology is essential to many of today’s AI experiences,… Read More »Apple Workshop on Natural Language and Interactive Systems 2025 Apple Machine Learning Research

​Recent advances in large language models (LLMs) have extended context lengths, enabling assistants to sustain long histories for coherent, personalized responses. This ability, however, hinges on Key-Value (KV) caching, whose memory grows linearly with dialogue length and quickly dominates under strict resource constraints. An active… Read More »EpiCache: Episodic KV Cache Management for Long Conversational Question Answering Apple Machine Learning Research

​This paper re-examines the first normalized incomplete moment, a well-established measure of inequality with wide applications in economic and social sciences. Despite the popularity of the measure itself, existing statistical inference appears to lag behind the needs of modern-age analytics. To fill this gap, we… Read More »Alternative Statistical Inference for the First Normalized Incomplete Moment Apple Machine Learning Research

​We present AToken, the first unified visual tokenizer that achieves both high-fidelity reconstruction and semantic understanding across images, videos, and 3D assets. Unlike existing tokenizers that specialize in either reconstruction or understanding for single modalities, AToken encodes these diverse visual inputs into a shared 4D… Read More »AToken: A Unified Tokenizer for Vision Apple Machine Learning Research

​[[{“value”:” This post was written with Sarah Ostermeier from Comet. As enterprise organizations scale their machine learning (ML) initiatives from proof of concept to production, the complexity of managing experiments, tracking model lineage, and managing reproducibility grows exponentially. This is primarily because data scientists and… Read More »Rapid ML experimentation for enterprises with Amazon SageMaker AI and Comet Vikesh Pandey Artificial Intelligence

​[[{“value”:” This post is co-written with Abhinav Pandey from Nippon Life India Asset Management Ltd. Accurate information retrieval through generative AI-powered assistants is a popular use case for enterprises. To reduce hallucination and improve overall accuracy, Retrieval Augmented Generation (RAG) remains the most commonly used… Read More »How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock Shailesh Shivakumar Artificial Intelligence