Mark true for all of the following statements that are correct about Transformers. Unlike with RNNs, the amount of learnable parameters in a transformer scales with the maximum sequence length of inputs it is trained on. If we remove all of the feedforward layers in a standard transformer, each output of our model at each timestep is a linear combination of the inputs. Without positional encodings, if you permute the input sequence to a transformer encoder, the resulting output sequence will be the output sequence of the original input, except permuted in the same way. In a single multi-head attention layer, the operations for each head can be run in parallel to the other heads (e.g. the operations for one head do not depend on the others).

Mark true for all of the following statements that are correct about Transformers. Unlike with RNNs, the amount of learnable parameters in a transformer scales with the maximum sequence length of inputs it is trained on. If we remove all of the feedforward layers in a standard transformer, each output of our model at each timestep is a linear combination of the inputs. Without positional encodings, if you permute the input sequence to a transformer encoder, the resulting output sequence will be the output sequence of the original input, except permuted in the same way. In a single multi-head attention layer, the operations for each head can be run in parallel to the other heads (e.g. the operations for one head do not depend on the others).

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Mark true for all of the following statements that are correct about Transformers.
Unlike with RNNs, the amount of learnable parameters in a transformer scales with the
maximum sequence length of inputs it is trained on.
If we remove all of the feedforward layers in a standard transformer, each output of our
model at each timestep is a linear combination of the inputs.
Without positional encodings, if you permute the input sequence to a transformer encoder,
the resulting output sequence will be the output sequence of the original input, except
permuted in the same way.
In a single multi-head attention layer, the operations for each head can be run in parallel to
the other heads (e.g. the operations for one head do not depend on the others).

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