Polymathic AI Releases ‘The Well’: 15TB of Machine Learning Datasets Containing Numerical Simulations of a Wide Variety of Spatiotemporal Physical Systems Asif Razzaq Artificial Intelligence Category – MarkTechPost

[[{“value”:” The development of machine learning (ML) models for scientific applications has long been hindered by the lack of suitable datasets that capture the complexity and diversity of physical systems. Many existing datasets are limited, often covering only small classes of physical behaviors. This lack… Read More »Polymathic AI Releases ‘The Well’: 15TB of Machine Learning Datasets Containing Numerical Simulations of a Wide Variety of Spatiotemporal Physical Systems Asif Razzaq Artificial Intelligence Category – MarkTechPost

Speed up your AI inference workloads with new NVIDIA-powered capabilities in Amazon SageMaker Abhishek Sawarkar AWS Machine Learning Blog

[[{“value”:” This post is co-written with Abhishek Sawarkar, Eliuth Triana, Jiahong Liu and Kshitiz Gupta from NVIDIA. At re:Invent 2024, we are excited to announce new capabilities to speed up your AI inference workloads with NVIDIA accelerated computing and software offerings on Amazon SageMaker. These… Read More »Speed up your AI inference workloads with new NVIDIA-powered capabilities in Amazon SageMaker Abhishek Sawarkar AWS Machine Learning Blog

Unlock cost savings with the new scale down to zero feature in SageMaker Inference Marc Karp AWS Machine Learning Blog

[[{“value”:” Today at AWS re:Invent 2024, we are excited to announce a new feature for Amazon SageMaker inference endpoints: the ability to scale SageMaker inference endpoints to zero instances. This long-awaited capability is a game changer for our customers using the power of AI and… Read More »Unlock cost savings with the new scale down to zero feature in SageMaker Inference Marc Karp AWS Machine Learning Blog

Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference Wenzhao Sun AWS Machine Learning Blog

[[{“value”:” Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI models for inference. This innovation allows you to scale your models faster, observing up to 56%… Read More »Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference Wenzhao Sun AWS Machine Learning Blog

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1 Lokeshwaran Ravi AWS Machine Learning Blog

[[{“value”:” The generative AI landscape has been rapidly evolving, with large language models (LLMs) at the forefront of this transformation. These models have grown exponentially in size and complexity, with some now containing hundreds of billions of parameters and requiring hundreds of gigabytes of memory.… Read More »Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1 Lokeshwaran Ravi AWS Machine Learning Blog

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2 Melanie Li AWS Machine Learning Blog

[[{“value”:” In Part 1 of this series, we introduced Amazon SageMaker Fast Model Loader, a new capability in Amazon SageMaker that significantly reduces the time required to deploy and scale large language models (LLMs) for inference. We discussed how this innovation addresses one of the major… Read More »Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2 Melanie Li AWS Machine Learning Blog

Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private Stochastic Gradient Descent (DP-SGD) Sana Hassan Artificial Intelligence Category – MarkTechPost

[[{“value”:” Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent process by clipping individual gradients to a fixed norm and adding noise to the aggregated gradients of… Read More »Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private Stochastic Gradient Descent (DP-SGD) Sana Hassan Artificial Intelligence Category – MarkTechPost

Towards Time-Series Reasoning with LLMs Apple Machine Learning Research

Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show… Read More »Towards Time-Series Reasoning with LLMs Apple Machine Learning Research

Strategic Linear Contextual Bandits Apple Machine Learning Research

Motivated by the phenomenon of strategic agents gaming a recommendation system to maximize the number of times they are recommended to users, we study a strategic variant of the linear contextual bandit problem, where the arms strategically misreport privately observed contexts to the learner. %… Read More »Strategic Linear Contextual Bandits Apple Machine Learning Research

Leveraging Periodicity for Robustness with Multi-modal Mood Pattern Models Apple Machine Learning Research

[[{“value”:”*Equal Contributors Data from wearable sensors (e.g., heart rate, step count) can be used to model mood patterns. We characterize feature representations and modeling strategies with multi-modal discrete time series data for mood pattern classification with a large dataset with naturalistic missingness (n=116,819 participants) using… Read More »Leveraging Periodicity for Robustness with Multi-modal Mood Pattern Models Apple Machine Learning Research

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