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放棄巴點睇SNN

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堆proof too long didnt read

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放棄巴點睇SNN

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堆proof too long didnt read

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新野嚟？有冇link?

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突發

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媽呀 個supervisor覆左cold email

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佢下星期香港有conference話可以見我

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佢咩U？你極想跟佢？係就開始狂J佢papers

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當時第一封cold email就咁講research interest
第二封我就睇左佢大約一半同重點既paper 寫左兩個research proposal send比佢
跟住就有reply了

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媽呀 我淨係cold email左佢一個 因為淨係想跟佢 我諗住冇reply就下年再算

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不過佢係你地睇唔起澳洲 melb u

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放棄巴點睇SNN

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堆proof too long didnt read

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新野嚟？有冇link?

Self-Normalizing Neural Networks

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遲啲睇睇

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太長唔quote太多

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以下講嘅我唔太肯定啱唔啱
通常討論sequence of random variables(in particular markov chain),我哋會將佢諗成一串sequence of numbers
而個dynamic就係將串數字移左或者移右一格
例如將
(x1,x2,x3,....)
依串數字shift一shift變
(x2,x3,x4,...)

ergodicity就話你知你不斷咁樣shift法，你會見到哂所有嘅"random pattern"(因為咁shift法會行勻成個space), 個randomness就measured by你個probability

你可以想像如果個markov chain要滿足咁嘅條件，佢就應該唔會係periodic,transient之類

[quote][quote][quote]

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太長唔quote太多

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以下講嘅我唔太肯定啱唔啱
通常討論sequence of random variables(in particular markov chain),我哋會將佢諗成一串sequence of numbers
而個dynamic就係將串數字移左或者移右一格
例如將
(x1,x2,x3,....)
依串數字shift一shift變
(x2,x3,x4,...)

ergodicity就話你知你不斷咁樣shift法，你會見到哂所有嘅"random pattern"(因為咁shift法會行勻成個space), 個randomness就measured by你個probability

你可以想像如果個markov chain要滿足咁嘅條件，佢就應該唔會係periodic,transient之類

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做咩派膠比你諗起果個人

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