Cs7643 Quiz 4 With Complete Questions And Answers Newest Rated A+ 2025/2026

Why Graph Embeddings {Ans: - They are a form of unsupervised learning on graphs - Results in task-agnostic entity representations - Features are useful on downstream tasks without much data - Nearest Neighbors are semantically meaningful}Word2vec Variants {Ans: Skip-Gram: Predict context words given center word Continuous Bag of Words: Predict center word from (bag of) context words}Evaluating Word Embeddings Intrinsic {Ans: - Evaluation on a specific/intermediate subtask - Fast to compute - Helps to understand the system - Not clear if really helpful unless correlation to real task is established Example: Evaluate word vectors by how well their cosine distance after addition captures intuitive semantic and syntactic analogy questions}Skip-Gram Model: Negative Sampling {Ans: - for each (w, c) pair, sample k negative pairs (w, c') - Maximize probability that outside word appears, minimize probability that random word appears - choose a distribution that samples less frequent words likely}RNNs and LSTMs, how their update rules differ, and what they problems each have or solve (vanishing and/or exploding gradients) {Ans: - Solving Vanishing Gradients: The way it does so is by creating an internal memory state which is simply added to the processed input, which greatly reduces