1|Page
CS 7643 QUIZ 4 (ACTUAL 2025/2026) QUESTIONS
AND VERIFIED ANSWERS/UPDATE| 100% CORRECT
Embedding - (answers)A learned map from entities to vectors that encodes
similarity
Graph Embedding - (answers)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 - (answers)- 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
Truncated Backpropagation through time - (answers)- Only backpropagate a RNN
through T time steps
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Recurrent Neural Networks (RNN) - (answers)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 - (answers)- 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 - (answers)- f(t) = forget gate
- i(t) = input gate
- u(t) = candidate update gate
- o(t) = output gate
- c(t) = cell state
- c(t) = f(t) * c(t - 1) + i(t) * u(t)
- h(t) = hidden state
CS 7643 QUIZ 4 (ACTUAL 2025/2026) QUESTIONS
AND VERIFIED ANSWERS/UPDATE| 100% CORRECT
Embedding - (answers)A learned map from entities to vectors that encodes
similarity
Graph Embedding - (answers)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 - (answers)- 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
Truncated Backpropagation through time - (answers)- Only backpropagate a RNN
through T time steps
, 2|Page
Recurrent Neural Networks (RNN) - (answers)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 - (answers)- 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 - (answers)- f(t) = forget gate
- i(t) = input gate
- u(t) = candidate update gate
- o(t) = output gate
- c(t) = cell state
- c(t) = f(t) * c(t - 1) + i(t) * u(t)
- h(t) = hidden state
