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ssc.io
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| | | | | Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable performance when confronted with diverse temporal patterns and complex data characteristics. We propose a novel approach to tackle the problem of domain generalization in time series forecasting. We focus on a scenario where time series domains share certain common attributes and exhibit no abrupt distribution shifts. Our method revolves around the incorpora... | |
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jalammar.github.io
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| | | | | Discussions: Hacker News (397 points, 97 comments), Reddit r/MachineLearning (247 points, 27 comments) Translations: German, Korean, Chinese (Simplified), Russian, Turkish The tech world is abuzz with GPT3 hype. Massive language models (like GPT3) are starting to surprise us with their abilities. While not yet completely reliable for most businesses to put in front of their customers, these models are showing sparks of cleverness that are sure to accelerate the march of automation and the possibilities of intelligent computer systems. Let's remove the aura of mystery around GPT3 and learn how it's trained and how it works. A trained language model generates text. We can optionally pass it some text as input, which influences its output. The output is generat... | |
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ai.googleblog.com
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criticalai.org
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| | | Angel McNall photography Adapting College Writing for the Age of Large Language Models such as ChatGPT: Some Next Steps for Educators (Updated 4/17/2023 to include new links) READERS INTERESTED IN THIS BLOG ARE ADVISED TO GO TO MORE UP-TO-DATE-RESOURCES INCLUDING OUR NEWSTUDENT GUIDEAND OUR UPDATED LIVING DOCUMENT, TEACHING CRITICAL AI LITERACIES. By Anna Mills and... | ||