Cs7643-Quiz 5 Questions And Verified Correct Answers Graded A-Mlatest Guaranteed Pass.docx

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)

- E[delta_theta * log_pi_theta(a | s) (Q_(PI_theta)(s, a))



Advantage Actor-critic - CORRECT ANSWER-- Uses Q minus V values (i.e. Advantage)

- E[delta_theta * log_pi_theta(a | s) (Q_(PI_theta)(s, a) - V_PI_theta)(s))



Semi-Supervised Learning - CORRECT ANSWER-- Some amounts of labeled data (10/20 per
category), but lots of unlabeled data



Self-Supervised Learning - CORRECT ANSWER-- A form of unsupervised learning

- No labels for representation learning



Few-Shot Learning - CORRECT ANSWER-- 1/5 per category, no unlabeled data



Traditional Unsupervised Learning Methods - CORRECT ANSWER-1. Density Estimation:
Modeling P(x)

- Deep Generative Models

, 2. Clustering: Comparing/Grouping

- Metric Learning & Clustering



3. Principle Component Analysis: Representation Learning. Some form of feature learning -->
closest to deep learning

- Almost all deep learning



Pseudo-labeling for Unlabeled Data (Semi-Supervised Learning) - CORRECT ANSWER-- Learn a
model on labeled training data

- Feed unlabeled example through model

- Take the most confident predictions and convert to pseudo label

- Put these pseudo labels into dataset and retrain

--> these will be more noisy than human labels



Cross-View/Augmentation & Consistency - CORRECT ANSWER-- Take an unlabeled example and
make weakly and strongly augmented data

- Use weakly-augment an image and get a pseudo-label

- Strongly-augment an image and make a prediction

- train these predictions on the labels from the weakly augmented data



Idea:

- Weak augmentation isn't so severe that the pseudo-labels are bad

- Using strong augmentation to make the NN learn better feature representations



Meta-Learning (Few-Shot Learning) - CORRECT ANSWER-- Learning to learn