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..
Deep Learning with Dynamic Computation Graphs
Authors: Moshe Looks, Marcello Herreshoff, DeLesley Hutchins, Peter Norvig
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results for our implementation in Tensor Flow. Section 3 presents a combinator library for concisely implementing models with DCGs using dynamic batching. The test results shown in Table 1 emphasize the importance of batching, especially on GPUs. |
| Researcher Affiliation | Industry | Moshe Looks, Marcello Herreshoff, De Lesley Hutchins & Peter Norvig Google Inc. EMAIL |
| Pseudocode | No | The paper describes algorithms conceptually but does not provide pseudocode blocks or sections explicitly labeled as "Algorithm". |
| Open Source Code | Yes | 1The library is called Tensor Flow Fold and lives at http://github.com/tensorflow/fold. |
| Open Datasets | Yes | We used constituency Tree-LSTMs with tuned Glove vectors for word embedding... Results are shown in Table 2, including the best previously reported results. Fine-grained accuracy is measured for all trees and calculated based on the five possible labels. Test set accuracies on the Stanford Sentiment Treebank |
| Dataset Splits | Yes | Noting the small size of this dataset (8544/1101/2210 trees for train/dev/test) |
| Hardware Specification | Yes | The CPU tests were run on a Dell z620 workstation with dual 8-core Intel Xeon processors (32 hardware threads), and the GPU tests were done using a consumer Nvidia Ge Force GTX-1080 card. |
| Software Dependencies | No | The paper mentions using "Tensor Flow" and the library "Tensor Flow Fold" but does not specify exact version numbers for these software dependencies, which would be necessary for full reproducibility. |
| Experiment Setup | Yes | The tree size is 128, with a state size of 1024 for the LSTM. We used constituency Tree-LSTMs with tuned Glove vectors for word embedding... and increased the LSTM state size from 150 to 300, leaving all other hyperparameters unchanged. |