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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2019-08-14 3 136 T01 this (.) um in our class↗
Oral Assessments Graduate 2019-08-14 3 137 UNK (0.7)
Oral Assessments Graduate 2019-08-14 3 138 S01 u:h (2.0) °⁎we⁎° in this class we'll first talk about
Oral Assessments Graduate 2019-08-14 3 139 S01 some basic concepts about the machine learning↘ uh after
Oral Assessments Graduate 2019-08-14 3 140 S01 you know that→ a:nd we will (.) ∆talk about∆ some basic ideas
Oral Assessments Graduate 2019-08-14 3 141 S01 of ∆adversarial machine learning∆ such as pgd and (.) uh
Oral Assessments Graduate 2019-08-14 3 142 S01 (0.7) some some other (0.5) very (0.4) traditional methods↘
Oral Assessments Graduate 2019-08-14 3 143 S01 and y- there are some (.) new progress in this field because
Oral Assessments Graduate 2019-08-14 3 144 S01 it's quite new area→ and: after this class you may view (.) uh
Oral Assessments Graduate 2019-08-14 3 145 S01 their papers if- uh if you (.) °are interested in that°↘
Oral Assessments Graduate 2019-08-14 3 146 T01 okay→
Oral Assessments Graduate 2019-08-14 3 147 S01 yep
Oral Assessments Graduate 2019-08-14 3 148 T01 what was it you said pgdd or something↗
Oral Assessments Graduate 2019-08-14 3 149 S01 u:h pgd ∇is called projection∇ uh gradient descent↘
Oral Assessments Graduate 2019-08-14 3 150 UNK (0.5)
Oral Assessments Graduate 2019-08-14 3 151 S01 just imagine it- it's a iterative gradient descent method (.) of
Oral Assessments Graduate 2019-08-14 3 152 S01 from the original benign image and (.) gradually follow the
Oral Assessments Graduate 2019-08-14 3 153 S01 gradient moves and (.) we will generate uh noise ⌈yeah↘
Oral Assessments Graduate 2019-08-14 3 154 T01 ⌊okay
Oral Assessments Graduate 2019-08-14 3 155 UNK (1.3)
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