Dance Dance Convolution
Authors: Chris Donahue, Zachary C. Lipton, Julian McAuley
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For both the Fraxtil and ITG datasets we apply 80%, 10%, 10% splits for training, validation, and test data, respectively. In Table 2, we list the results of our experiments for step placement. In Table 3 we present results for the step selection task. |
| Researcher Affiliation | Academia | 1 UCSD Department of Music, San Diego, CA 2 UCSD Department of Computer Science, San Diego, CA. |
| Pseudocode | No | The paper describes the architecture of the models and training procedures, but does not provide pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1 https://github.com/chrisdonahue/ddc |
| Open Datasets | Yes | We contribute two large public datasets, which we consider to be of notably high quality and consistency.1 Each dataset is a collection of recordings and step charts. One contains charts by a single author and the other by multiple authors. 1https://github.com/chrisdonahue/ddc |
| Dataset Splits | Yes | For both the Fraxtil and ITG datasets we apply 80%, 10%, 10% splits for training, validation, and test data, respectively. |
| Hardware Specification | Yes | All models satisfy this criteria within 12 hours of training on a single machine with an NVIDIA Tesla K40m GPU. |
| Software Dependencies | No | The paper mentions using the 'ESSENTIA library (Bogdanov et al., 2013)', but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We train with mini-batches of size 256, scaling down gradients when their l2 norm exceeds 5. We apply 50% dropout following each LSTM and fully connected layer. For recurrent neural networks, we calculate updates using backpropagation through time with 100 steps of unrolling. For all neural network models, we learn parameters by minimizing cross-entropy. We train with mini-batches of size 64, and scale gradients using the same scheme as for step placement. We use 50% dropout during training for both the MLP and RNN models. |