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Shashank Suhas
seminar-breakout
Commits
178f3611
Commit
178f3611
authored
Jul 05, 2016
by
Yuxin Wu
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fix image links
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examples/Atari2600/README.md
examples/Atari2600/README.md
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examples/DisturbLabel/README.md
examples/DisturbLabel/README.md
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examples/ResNet/README.md
examples/ResNet/README.md
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examples/Atari2600/README.md
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@@ -8,7 +8,7 @@ and Double-DQN in:
...
@@ -8,7 +8,7 @@ and Double-DQN in:
Can reproduce the claimed performance, on several games I've tested with.
Can reproduce the claimed performance, on several games I've tested with.


A demo trained with Double-DQN on breakout is available at
[
youtube
](
https://youtu.be/o21mddZtE5Y
)
.
A demo trained with Double-DQN on breakout is available at
[
youtube
](
https://youtu.be/o21mddZtE5Y
)
.
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...
examples/DisturbLabel/README.md
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@@ -7,7 +7,7 @@ As many, I didn't believe the method and the results at first.
...
@@ -7,7 +7,7 @@ As many, I didn't believe the method and the results at first.
This is a simple mnist training script with DisturbLabel. It uses the architecture in the paper and
This is a simple mnist training script with DisturbLabel. It uses the architecture in the paper and
hyperparameters in my original
[
mnist example
](
examples/mnist-convnet.py
)
. The results surprised me:
hyperparameters in my original
[
mnist example
](
examples/mnist-convnet.py
)
. The results surprised me:


Experiements are repeated 15 times for p=0, 10 times for p=0.02 & 0.05, and 5 times for other values
Experiements are repeated 15 times for p=0, 10 times for p=0.02 & 0.05, and 5 times for other values
of p. All experiements run for 100 epochs, with lr decay, which are enough for them to converge.
of p. All experiements run for 100 epochs, with lr decay, which are enough for them to converge.
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examples/ResNet/README.md
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@@ -7,7 +7,7 @@ with the variants proposed in "Identity Mappings in Deep Residual Networks", [ht
...
@@ -7,7 +7,7 @@ with the variants proposed in "Identity Mappings in Deep Residual Networks", [ht
The train error shown here is a moving average of the error rate of each batch in training.
The train error shown here is a moving average of the error rate of each batch in training.
The validation error here is computed on test set.
The validation error here is computed on test set.


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