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CS 7643 QUIZ 4 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
- The size of the network grows with the maximum allowed size of
the input or output sequences
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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
CS 7643 QUIZ 4 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
- The size of the network grows with the maximum allowed size of
the input or output sequences
, Page | 2
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
