CS7643 Quiz 4 Exam 2026 Questions and
Answers
Embedding - Correct answer-A learned map from entities to vectors that encodes
similarity
Graph Embedding - Correct answer-Optimize the objective that connected nodes
have more similar embeddings than unconnected nodes.
Task: convert nodes to vectors
- effectively unsupervised learning where nearest neighbors are similar
- these learned vectors are useful for downstream tasks
Multi-layer Perceptron (MLP) pain points for NLP - Correct answer-- Cannot
easily support variable-sized sequences as inputs or outputs
- No inherent temporal structure
- No practical way of holding state
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, - The size of the network grows with the maximum allowed size of the input or
output sequences
Truncated Backpropagation through time - Correct answer-- Only backpropagate a
RNN through T time steps
Recurrent Neural Networks (RNN) - Correct answer-h(t) = activation(U*input +
V*h(t-1) + bias)
y(t) = activation(W*h(t) + bias)
- activation is typically the logistic function or tanh
- outputs can also simply be h(t)
- family of NN architectures for modeling sequences
Training Vanilla RNN's difficulties - Correct answer-- Vanishing gradients
- Since dx(t)/dx(t-1) = w^t
- if w > 1: exploding gradients
- if w < 1: vanishing gradients
Long Short-Term Memory Network Gates and States - Correct answer-- f(t) =
forget gate
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Answers
Embedding - Correct answer-A learned map from entities to vectors that encodes
similarity
Graph Embedding - Correct answer-Optimize the objective that connected nodes
have more similar embeddings than unconnected nodes.
Task: convert nodes to vectors
- effectively unsupervised learning where nearest neighbors are similar
- these learned vectors are useful for downstream tasks
Multi-layer Perceptron (MLP) pain points for NLP - Correct answer-- Cannot
easily support variable-sized sequences as inputs or outputs
- No inherent temporal structure
- No practical way of holding state
©COPYRIGHT 2025, ALL RIGHTS RESERVED 1
, - The size of the network grows with the maximum allowed size of the input or
output sequences
Truncated Backpropagation through time - Correct answer-- Only backpropagate a
RNN through T time steps
Recurrent Neural Networks (RNN) - Correct answer-h(t) = activation(U*input +
V*h(t-1) + bias)
y(t) = activation(W*h(t) + bias)
- activation is typically the logistic function or tanh
- outputs can also simply be h(t)
- family of NN architectures for modeling sequences
Training Vanilla RNN's difficulties - Correct answer-- Vanishing gradients
- Since dx(t)/dx(t-1) = w^t
- if w > 1: exploding gradients
- if w < 1: vanishing gradients
Long Short-Term Memory Network Gates and States - Correct answer-- f(t) =
forget gate
©COPYRIGHT 2025, ALL RIGHTS RESERVED 2
