Artificial Intelligence & Machine Learning

Advanced Generative AI Proficiency Test

Nine tough questions probing cutting-edge generative techniques for machine learning specialists.

#Generative AI#Autoregressive Models#Mode Seeking#FID Metric#RLHF#Transformer Decoding#VAE Reparameterization#GAN Training#Diffusion Models

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Total Questions

Difficulty Level

hard

Progress

0/0

Quiz Questions

Answer all questions below and test your knowledge.

1
In Denoising Diffusion Probabilistic Models (DDPMs), the reverse process approximates the posterior q(z_{t-1}|z_t, x) using what parameterized distribution?
Question 1
2
Spectral normalization in GANs constrains the discriminator's Lipschitz constant to prevent what common training instability?
Question 2
3
In Variational Autoencoders, the reparameterization trick enables backpropagation through which stochastic layer?
Question 3
4
What key innovation in the original Transformer architecture enables parallel autoregressive generation in models like GPT?
Question 4
5
Reinforcement Learning from Human Feedback (RLHF) primarily optimizes LLMs using what objective beyond supervised fine-tuning?
Question 5
6
The Fréchet Inception Distance (FID) evaluates generative models by measuring distance between what two multivariate Gaussians?
Question 6
7
In Progressive GANs, training begins with low-resolution images to mitigate what issue in high-dimensional generation?
Question 7
8
For flow-based generative models like Glow, the exact likelihood computation relies on what property of the transformation?
Question 8
9
In classifier-free guidance for diffusion models, the guidance scale ω amplifies conditioning by scaling what term?
Question 9

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Advanced Generative AI Proficiency Test

Quiz Intelligence

Quiz Questions

In Denoising Diffusion Probabilistic Models (DDPMs), the reverse process approximates the posterior q(z_{t-1}|z_t, x) using what parameterized distribution?

Spectral normalization in GANs constrains the discriminator's Lipschitz constant to prevent what common training instability?

In Variational Autoencoders, the reparameterization trick enables backpropagation through which stochastic layer?

What key innovation in the original Transformer architecture enables parallel autoregressive generation in models like GPT?

Reinforcement Learning from Human Feedback (RLHF) primarily optimizes LLMs using what objective beyond supervised fine-tuning?

The Fréchet Inception Distance (FID) evaluates generative models by measuring distance between what two multivariate Gaussians?

In Progressive GANs, training begins with low-resolution images to mitigate what issue in high-dimensional generation?

For flow-based generative models like Glow, the exact likelihood computation relies on what property of the transformation?

In classifier-free guidance for diffusion models, the guidance scale ω amplifies conditioning by scaling what term?

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