all the experiments in the paper approximate Hᴷ(x | θ) using bits under arithmetic coding, which is equivalent to cross-entropy here Hᴷ(x) is approximated using the formula for entropy of random uniform distribution for synthetic, and AC/xentropy of 'reference model' for text
MACHINE LEARNING
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AI-Driven Production Planning Solves EV Battery Demand Challenges
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With unpredictable #EV demand, battery planning is complex for automakers. Explore how #AI-driven production planning turns challenges into opportunities: https://
okt.to/qgSnw0 @mvollmer1 @antgrasso @chuckdbrooks @blueyonder @alvinfoo @BetaMoroney @TamaraMcCleary @EvanKirstel -
Understanding Neural Tangent Kernels in Machine Learning
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well jokes on both of you because i still don't understand neural tangent kernels
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NeurIPS submissions surge 10x: Is AI science accelerating?
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▂▃▆ is AI really experiencing explosive growth? this year over 25,000 papers were submitted to NeurIPS, up over 10x from 2,400 in 2016. poll for AI researchers: do you think AI *science* is progressing more quickly, more slowly, or at the same pace?
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Tesla Optimus: Musk and Huang Bet on AI Robotics Trillions
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« Plus gros produit de l’histoire ».
— VISION IA (@vision_ia) 7 juin 2025
Musk insiste, Jensen Huang confirme : Optimus.
Un seul réseau neuronal, tâches en rafale, IA = robotique. Quand Tesla verrouille ce levier, les trillions deviennent plausibles. https://t.co/0lhdPjp1rc pic.twitter.com/42GT0B6bEe« Plus gros produit de l’histoire ». Musk insiste, Jensen Huang confirme : Optimus. Un seul réseau neuronal, tâches en rafale, IA = robotique. Quand Tesla verrouille ce levier, les trillions deviennent plausibles.
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Experimental Results and Equations in AI Research
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i tried this one first, it's more of a collection of equations; the experimental results are pretty important here…
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Neural Tangent Kernel: Timeless Mathematical Foundation vs LLM Research
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maybe, but unlike most research e.g. in LLMs, the neural tangent kernel is simply *true* and thus will still be relevant in 5 or 10 or 50 years
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Neural Tangent Kernel: Understanding Deep Learning Foundations
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most foundational concept in deep learning that no one understands is probably the Neural Tangent Kernel (NTK) this line of work studies neural networks of *infinite width*, which explain a lot about normal finite-width NNs and there is exactly one Very Good blog post on them:
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Kaggle’s Intro to Machine Learning course review
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Intro to ML by Kaggle is underrated. Fast and gets you past theory paralysis.
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Real-Time AI Performance Through Edge Computing and Data Streaming
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With 35K data points per second, bonded streams, and AI at the edge, performance emerges as the natural outcome of systems engineered for real-time orchestration under operational pressure. #TFBxSailGP #TFBPartner