CS7643 Quiz 7 VERIFIED
QUESTIONS AND ANSWERS 2025.
, Discriminative vs. Generative Models - CORRECT ANSWER-1.
Discriminative models model P(y|x)
- NN, SVM, etc.
- No way to handle unreasonable outputs (i.e. if trained on cats and
dog images, will always output a label of cat or dog)
2. Generative Models model P(x)
- Can parameterize our model as P(x, theta) and use Maximum
Likelihood Estimation to optimize the parameters
- Called generative because they can generate samples
- Can "reject" unreasonable inputs as being too unlikely
- Feature Learning without labels
Explicit vs Implicit Density - CORRECT ANSWER-Explicit: Explicitly
define and solve for Pmodel(x|θ), given an explicit likelihood that
can be maximized
Implicit: Learn a parameterized generation model that can sample
from the joint distribution Pmodel(x|θ) without explicitly defining
Pmodel(x|θ)
Tractable Density Estimation - CORRECT ANSWER-- Explicit Density
- Simplify joint distribution into factorized model of simpler
components and optimize with respect to simpler components
- PixelRNN/PixelCNN
Approximate Density Estimation - CORRECT ANSWER-- Explicit
Density
QUESTIONS AND ANSWERS 2025.
, Discriminative vs. Generative Models - CORRECT ANSWER-1.
Discriminative models model P(y|x)
- NN, SVM, etc.
- No way to handle unreasonable outputs (i.e. if trained on cats and
dog images, will always output a label of cat or dog)
2. Generative Models model P(x)
- Can parameterize our model as P(x, theta) and use Maximum
Likelihood Estimation to optimize the parameters
- Called generative because they can generate samples
- Can "reject" unreasonable inputs as being too unlikely
- Feature Learning without labels
Explicit vs Implicit Density - CORRECT ANSWER-Explicit: Explicitly
define and solve for Pmodel(x|θ), given an explicit likelihood that
can be maximized
Implicit: Learn a parameterized generation model that can sample
from the joint distribution Pmodel(x|θ) without explicitly defining
Pmodel(x|θ)
Tractable Density Estimation - CORRECT ANSWER-- Explicit Density
- Simplify joint distribution into factorized model of simpler
components and optimize with respect to simpler components
- PixelRNN/PixelCNN
Approximate Density Estimation - CORRECT ANSWER-- Explicit
Density