Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Hierarchical Model for Device Placement
Authors: Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with widely-used computer vision and natural language models show that our algorithm can find optimized, non-trivial placements for Tensor Flow computational graphs with over 80,000 operations. |
| Researcher Affiliation | Industry | |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology was found. |
| Open Datasets | Yes | For a fair comparison to previous state-of-the-art deep RL methods (Mirhoseini et al., 2017), we use the same model architectures (Inception-V3, RNNLM, and 2-Layer NMT models), hyperparameters and input data. In addition, we evaluate our model on a 152-layer Res Net (He et al., 2016) with Image Net data (Deng et al., 2009), as well as more complex NMT models with 4 and 8 layers. |
| Dataset Splits | No | The paper uses well-known models (e.g., Inception-V3, ResNet) and mentions ImageNet data, but does not provide specific training/validation/test dataset split percentages, sample counts, or explicit instructions for how to partition the data for reproducibility. |
| Hardware Specification | Yes | Our experiments are run on machines with 1 Intel Haswell 2300 CPU and up to 8 Nvidia Tesla K40 GPUs. |
| Software Dependencies | Yes | We use TensorFlow r1.3 to run our experiments. |
| Experiment Setup | Yes | We train both policies using Adam (Kingma & Ba, 2015) optimizer with a fixed learning rate of 0.1, gradient clipping of norm 1.0, tanh constant C = 5.0, and temperature T = 10.0. The number of Grouper and Placer samples in Eqs. 4 and 6 are m = 1 and k = 4, respectively. |