Barret Zoph, Quoc Le Google Brain
We lifted 5 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| TreeLimes/QANAS | extension | 3 of 3 |
| carpedm20/ENAS-pytorch | reimplementation | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| add_argument_group | Ran | carpedm20/ENAS-pytorch/config.py code served (permissive licence) · get_code("6d80592fd6d47b2d") |
| get_data | Ran | TreeLimes/QANAS/performance_prediction.py pointer only (licence: NONE) · get_code("3ec9da1cd4329956") |
| int_or_float | Ran | TreeLimes/QANAS/performance_prediction.py pointer only (licence: NONE) · get_code("75b2d837ccdfa9c0") |
| permute_data | Ran | TreeLimes/QANAS/performance_prediction.py pointer only (licence: NONE) · get_code("a20bae4c047fd876") |
| str2bool | Ran | carpedm20/ENAS-pytorch/config.py code served (permissive licence) · get_code("e5b1aff86a339d0e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214. * Work done as a member of the Google Brain Residency program (g.co/brainresidency.)
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1611.01578")
get_code_for_paper("1611.01578")
have("1611.01578")
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