#Models

When talking to people who haven’t deployed ML models, I keep hearing a lot of misperceptions about ML models in production. Here are a few of them.(1/6) 1. Deploying ML
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I don't get the argument made against training bigger models from the environmental perspective. Given breakthroughs like AlphaFold, do people really think they can weigh the opportunity cost of n-th
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People are arguing about 3D backgrounds in comics on my TL, I think it's an interesting subject.I've used 3d models here and there and found that while it's very useful
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I feel like we shouldn’t be making people scared. What happened to PH sucks for some peoples’ income but they had plenty of time to create a safe work space
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[Serious] Our goal for this NHL season is for people who aren't on twitter to not misinterpret us when we talk about our models. Kind of. It gets annoying Fuck
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I’ve been reading some neo-classical econometrics papers from 80-90s. Hansen-Singleton (Econometrica)And Berry-Levinnshon-Pakes (Econometrica). They start out with stochastic economic models (to define structures/ Causal models). 1/n They
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Vallance says the models produced “are not forecasts, they’re models which tell you how things can look. But they’re not forecasts and they should be looked at knowing that. Hmm.
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An interesting paper on lessons from Foot and Mouth outbreak in UK, and mathematical modelling:https://www.research.ed.ac.uk/portal/en/publications/use-and-abuse-of-mathematical-models-an-illustration-from-the-2001-foot-and-mouth-disease-ep
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"The claims about... new viral mutations are wholly sourced to epidemiological models constructed by a handful of academics, who have access to influential people and platforms. These models have not
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a lot of you know my past between 2017-2019... i used to be a massive jb fan who made loads of jailbreak fan games (which explains why i do have
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Sensible and silly criticisms of modelling. (1/7) Sensible: The model misses out some important feature of reality, or is needlessly complex. This is hard to get right, so usually has
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9/ Data cannot help you select between hypotheses, it can only help you eliminate. The idea that theories can never be proved true, but only be shown to be wrong
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The COVIDhub Ensemble model that combines all the models did not perform well over the past 2 months.This is due to the fact that the majority of model submissions did
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A critical error that I see many grad students make: they try to estimate Frankenstein's model. Rather than viewing a model as answering a research question, they view a model
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There were to be 125 million Covid infections in India by mid-May, according to some estimations. What happened? Mathematical models, esp. when coming from big scientists are usually taken at
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living in a simulacrum, enacting models instead of innovating, *simulating* everything instead of doing it, means that most ppl now act based solely on *intentions*, and the outcomes from those
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