Experimental News Clipping Site

Tag: training methodologies

  • Hacker News: Bayesian Neural Networks

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    Source URL: https://www.cs.toronto.edu/~duvenaud/distill_bayes_net/public/ Source: Hacker News Title: Bayesian Neural Networks Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses Bayesian Neural Networks (BNNs) and their ability to mitigate overfitting and provide uncertainty estimates in predictions. It contrasts standard neural networks, which are flexible yet prone to overfitting, with BNNs that utilize Bayesian…

  • Slashdot: HarperCollins Confirms It Has a Deal to Sell Authors’ Work to AI Company

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    Source URL: https://slashdot.org/story/24/11/18/2142209/harpercollins-confirms-it-has-a-deal-to-sell-authors-work-to-ai-company?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: HarperCollins Confirms It Has a Deal to Sell Authors’ Work to AI Company Feedly Summary: AI Summary and Description: Yes Summary: HarperCollins has initiated a controversial partnership with an AI technology firm, allowing limited use of select nonfiction titles for training AI models. Authors can opt in for a…

  • Hacker News: Diffusion models are evolutionary algorithms

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    Source URL: https://gonzoml.substack.com/p/diffusion-models-are-evolutionary Source: Hacker News Title: Diffusion models are evolutionary algorithms Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a groundbreaking paper linking diffusion models and evolutionary algorithms, positing that both processes create novelty and generalization in data. This revelation is crucial for AI professionals, particularly in generative AI and…

  • Hacker News: Data movement bottlenecks to large-scale model training: Scaling past 1e28 FLOP

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    Source URL: https://epochai.org/blog/data-movement-bottlenecks-scaling-past-1e28-flop Source: Hacker News Title: Data movement bottlenecks to large-scale model training: Scaling past 1e28 FLOP Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The provided text explores the limitations and challenges of scaling large language models (LLMs) in distributed training environments. It highlights critical technological constraints related to data movement both…