cvpr maths123

Job Description:

You are training a deep neural network on a binary classification task, using a

sigmoid activation in the final layer, and a mixture of tanh and ReLU activations

for all other layers. You notice that the gradients to these layers completely go to

zero very early on in training. Which of the following fixes could help?

• Increase the size of your training set

• Switch the ReLU activations with leaky ReLUs everywhere

• Add Batch Normalization before every activation

• Changing the SGD to adam optimizer

At test time, a Dropout layer with a dropout probability α will:

• Multiply the input tensor by α.

• Drop neurons from the input with probability α.

• Power normalizes the input tensor by α.

• None of the above.

If your input to the convolutional layer is 32 × 32 × 16, how many parameters are

there in a single 1 × 1 convolution filter, including bias?

• 2

• 17

• 1025

• 1

Which methods can be used to prevent overfitting?

• Changing scheduler

• Changing batch size

• L2 regularization

• Dropout

A (32 × 32 × 3) image is passed through a convolutional layer with 32 filters, each

of size (3 × 3). The padding size is 1 and the stride size is 4. What is the size of the

output feature map?

• (32 × 32 × 3)

• (16 × 16 × 32)

• (8 × 8 × 32)

• (32 × 32 × 32)

The questions in this section should be answered in maximum 4 sentences. Please

be concise in your responses.

Your project is in collaboration with the police, and they want you to

design an AI system to identify criminals, given their data. Since being imprisoned

is a severe punishment, it is very important for AI system to not incorrectly identify

the criminals, and simultaneously ensure that the place is as safe as possible. What

could be the most appropriate evaluation metric and why: Accuracy, Precision,

Mean average precision, or F1 score?

You are solving a face recognition (modeled as binary classification) that uses facial

data to help users log into their devices. You train a classification model for user X

until it achieves 98% accuracy on the validation set for same user. However, upon

deployment (at test time) the model has a 50% misclassification rate. List the two

most important factors for the differences in performance between the validation set

and test set

Pooling layers such as Max or Sum pooling in Deep Nets cause a loss of information

between convolutional layers, why would we add Pooling layers to our network?

Why do we need activation functions (ReLU etc) in neural networks?

A medical company approaches you for deep learning expertise. They want to

classify X-ray images into 2 classes (disease or healthy). In the given dataset the

disease class is very underrepresented (1 disease for every 10 healthy). Precisely

describe two techniques that you can use during training that help alleviate the

class imbalance problem.

Навыки: Matlab and Mathematica, Электротехника

ID проекта: #35708070