Difference between revisions of "Deep Double Descent Where Bigger Models and More Data Hurt"

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(Created page with "== Presented By == Sam Senko, Tyler Verhaar and Ben Zhang")
 
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== Presented By ==
 
== Presented By ==
 
Sam Senko, Tyler Verhaar and Ben Zhang
 
Sam Senko, Tyler Verhaar and Ben Zhang
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== Models and Experiments ==
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A variety of experiments were done to demonstrate the double descent phenomenon and its connection to effective model complexity in a number of different situations. Three main model architectures were used in these experiments:
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# A simple convolutional neural network with 4 convolutional layers and 1 fully connected layer. The widths of the convolutional layers were k, 2k, 4k and 8k respectively where k is a parameter which was varied in the experiments.
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# Resnets, introduced in (He, et al., 2016), with the convolutional layers having widths k, 2k, 4k and 8k respectively where k is again a parameter
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# Transformers, a type of recurrent neural network often used in natural language processing. This used a 6-layer architecture. The embedding dimension was varied to vary the complexity of the model and the width of the fully connected layers was scaled proportionately to this embedding dimension.
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The above models were trained with variants of gradient descent, with the number of gradient steps varying from around 5 thousand to around 500 thousand depending on the particular model and the experiment. In some experiments, label noise was used where, with probability p, the label was replaced by an incorrect label chosen uniformly at random.

Revision as of 13:52, 18 November 2021

Presented By

Sam Senko, Tyler Verhaar and Ben Zhang

Models and Experiments

A variety of experiments were done to demonstrate the double descent phenomenon and its connection to effective model complexity in a number of different situations. Three main model architectures were used in these experiments:

  1. A simple convolutional neural network with 4 convolutional layers and 1 fully connected layer. The widths of the convolutional layers were k, 2k, 4k and 8k respectively where k is a parameter which was varied in the experiments.
  2. Resnets, introduced in (He, et al., 2016), with the convolutional layers having widths k, 2k, 4k and 8k respectively where k is again a parameter
  3. Transformers, a type of recurrent neural network often used in natural language processing. This used a 6-layer architecture. The embedding dimension was varied to vary the complexity of the model and the width of the fully connected layers was scaled proportionately to this embedding dimension.

The above models were trained with variants of gradient descent, with the number of gradient steps varying from around 5 thousand to around 500 thousand depending on the particular model and the experiment. In some experiments, label noise was used where, with probability p, the label was replaced by an incorrect label chosen uniformly at random.