In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Input: Consider Input: We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. 
 Question: What are the baselines?

Output: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6


Input: Consider Input: As fig:fit shows, estimated test accuracy is highly correlated with actual test accuracy for various datasets, with worst-case values $\mu <1\%$ and $\sigma <5\%$ . Note that the number of free parameters is small ($||\le 6$) compared to the number of points (42–49 model-data configurations), demonstrating the appropriateness of the proposed function for modeling the complex error landscape. 
 Question: What is proof that proposed functional form approximates well generalization error in practice?

Output: estimated test accuracy is highly correlated with actual test accuracy for various datasets appropriateness of the proposed function for modeling the complex error landscape


Input: Consider Input: We varied the number of experts between models, using ordinary MoE layers with 4, 32 and 256 experts and hierarchical MoE layers with 256, 1024 and 4096 experts. 
 Question: How is the correct number of experts to use decided?


Output: varied the number of experts between models