Guide-yakabatana mibvunzo · Pakati Nhanho

Nhengo Inference Inorwisa Quiz

Kudzidza kudzidziswa-nhengo inference, ine mimvuri modhi, LiRA calibration, mutsauko-kuvanzika miganhu, nhema positives, uye kurwisa ongororo.

Nzira dzekutungamira dzinoenderanaNhengo Inference Inorwisa
Mubvunzo 1 ye 8

Chii chinonzi nhengo inference kurwisa kuyedza kuziva?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Chii chinonzi nhengo inference kurwisa kuyedza kuziva?
    • Kana rekodhi chaiyo yaive chikamu chekudzidziswa seti
    • Kurongeka kwemuenzaniso
    • Mavakirwo emuenzaniso
    • Saizi yetest set
  2. How can overfitting affect membership-inference exposure in studied models?
    • It proves every training record is identifiable with certainty
    • It removes the need to evaluate attack errors
    • It can increase the behavioral difference between members and comparable nonmembers
    • It makes all outputs independent of training data
  3. Ndeipi nzira yakaita yepakutanga 2017 Shokri et al. kurwisa kuvimba?
    • Gradient clipping
    • Shadow models
    • Beam search
    • Kukamura Mazwi
  4. Sei kufungidzira kuve nhengo kuchigona kukuvadza kunyangwe pasina kuburitsa zviri murekodhi?
    • Inononoka modhi pasi
    • Inoshandura huremu hwemuenzaniso
    • Inobvisa data yekudzidziswa
    • Kuve nhengo pachayo kunogona kuburitsa chokwadi chakakomba, senge kuongororwa
  5. What calibration distinguishes the cited LiRA approach from a single naive loss threshold?
    • A guarantee that every record has the same inherent difficulty
    • Only an increased generation temperature
    • Per-example score distributions estimated from in and out models
    • A requirement that the target reveal its entire training dataset
  6. What does a properly specified differential-privacy guarantee bound?
    • Changes in output probabilities between neighboring datasets
    • A guarantee that no attack can ever make any correct guess
    • A mandatory deletion of all model outputs
    • Only the raw number of files in a training folder
  7. An attack incorrectly flags 10 of 1,000 actual nonmembers as members. What is its false-positive rate on those nonmembers?
    • 10%
    • 0.1%
    • 100%
    • 1%
  8. Why does the LiRA paper emphasize true-positive rate at low false-positive rates?
    • False positives are impossible by definition
    • Average accuracy can conceal whether confidently flagged records are reliable membership evidence
    • Every member and nonmember population must have identical sizes
    • The model’s training accuracy directly gives attack precision