Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

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3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view …

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Child support systems modernization: The time is now

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The majority of today’s child support systems are dated, first-generation systems that are now more than 25 years old. These systems need modernization to meet the evolving needs of children and families in the 21st century. With more than 20% of families and children supported by these systems, the impact is significant. Today’s constituents are …

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Google Research embarks on effort to map a mouse brain

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Posted by Michał Januszewski, Research Scientist, Google Research The human brain is perhaps the most computationally complex machine in existence, consisting of networks of billions of cells. Researchers currently don’t understand the full picture of how glitches in its network machinery contribute to mental illnesses and other diseases, such as dementia. However, the emerging connectomics …

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Build and deploy ML inference applications from scratch using Amazon SageMaker

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As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This post shows you how to build and host an ML application with custom containers …

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HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion

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Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise as a high-fidelity and compact representation. However, the lack of a regular and explicit grid structure also makes it challenging to apply generative modeling directly on implicit neural fields in …

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Unleashing the power of Presto: The Uber case study

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The magic behind Uber’s data-driven success Uber, the ride-hailing giant, is a household name worldwide. We all recognize it as the platform that connects riders with drivers for hassle-free transportation. But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Every …

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Innovation for Inclusion: Hack.The.Bias with Amazon SageMaker

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This post was co-authored with Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich. Everyone can easily get started with machine learning (ML) using Amazon SageMaker JumpStart. In this post, we show you how a university Hackathon team used SageMaker JumpStart to quickly build an application that helps users identify and remove …

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Improving your LLMs with RLHF on Amazon SageMaker

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Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. The technique operates by training a “reward model” based on human feedback and uses this model as a reward function to optimize an agent’s policy through reinforcement learning …

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