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Optimize PyTorch training performance with Reduction Server on Vertex AI

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As deep learning models become increasingly complex and datasets larger, distributed training is all but a necessity. Faster training makes for faster iteration to reach your modeling goals. But distributed training comes with its own set of challenges. On top of deciding what kind of distribution strategy you want to use and making changes to …

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Mix-and-match kit could enable astronauts to build a menagerie of lunar exploration bots

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The Walking Oligomeric Robotic Mobility System, or WORMS, is a reconfigurable, modular, multiagent robotics architecture for extreme lunar terrain mobility. The system could be used to assemble autonomous worm-like parts into larger biomimetic robots that could explore lava tubes, steep slopes, and the moon’s permanently shadowed regions.

Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis

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Pre-trained model representations have demonstrated state-of-the-art performance in speech recognition, natural language processing, and other applications. Speech models, such as Bidirectional Encoder Representations from Transformers (BERT) and Hidden units BERT (HuBERT), have enabled generating lexical and acoustic representations to benefit speech recognition applications. We investigated the use of pre-trained model representations for estimating dimensional emotions, …

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GPT-4

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We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.

Building a Media Understanding Platform for ML Innovations

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By Guru Tahasildar, Amir Ziai, Jonathan Solórzano-Hamilton, Kelli Griggs, Vi Iyengar Introduction Netflix leverages machine learning to create the best media for our members. Earlier we shared the details of one of these algorithms, introduced how our platform team is evolving the media-specific machine learning ecosystem, and discussed how data from these algorithms gets stored in …

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TAF

Learning from deep learning: a case study of feature discovery and validation in pathology

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Posted by Ellery Wulczyn and Yun Liu, Google Research When a patient is diagnosed with cancer, one of the most important steps is examination of the tumor under a microscope by pathologists to determine the cancer stage and to characterize the tumor. This information is central to understanding clinical prognosis (i.e., likely patient outcomes) and …

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arunkumar Lokh

Maximize performance and reduce your deep learning training cost with AWS Trainium and Amazon SageMaker

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

Today, tens of thousands of customers are building, training, and deploying machine learning (ML) models using Amazon SageMaker to power applications that have the potential to reinvent their businesses and customer experiences. These ML models have been increasing in size and complexity over the last few years, which has led to state-of-the-art accuracies across a …

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Building the most open and innovative AI ecosystem

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The future of artificial intelligence (AI) will be open. New and innovative capabilities in areas like generative AI will come from all corners of the technology ecosystem and from companies distributed around the globe — from early stage startups to cloud-native AI platforms to large, global enterprises. The momentum in this space is incredible. In …

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How AI is Shaping a New Era of Connectivity

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From metaverse digital twins powering predictive maintenance and network performance optimization to new telco business models born of 5G and edge services, AI applications can drive new operational efficiencies and revenue opportunities across the telecommunications industry. NVIDIA GTC, a global conference for the era of AI and the metaverse running online March 20-23, will showcase …

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RGI: Robust GAN-inversion for Mask-free Image Inpainting and Unsupervised Pixel-wise Anomaly Detection

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Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image restoration under corruptions. However, the performance of GAN-inversion can be limited by a lack of robustness to unknown gross …

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