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..
Differentiable Programs with Neural Libraries
Authors: Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that this leads to lifelong learning systems that transfer knowledge to new tasks more effectively than baselines. The experimental results are presented in Sec. 6. |
| Researcher Affiliation | Industry | 1Microsoft Research, Cambridge, UK 2Google Brain, Montréal, Canada (work done while at Microsoft). Correspondence to: Alexander L. Gaunt <EMAIL>. |
| Pseudocode | Yes | Figure 1 shows 'Learned operations' and an 'Instruction Set Declaration & initialization' block, including variable declarations, loops, and conditional statements, formatted like pseudocode. |
| Open Source Code | No | The paper provides a link for the Neural GPU baseline ('available at https://github.com/tensorflow/models/tree/master/neural_gpu') but does not state that the code for their own method (NTPT) is open-source or publicly available. |
| Open Datasets | Yes | ADD2X2 scenario: The first scenario in Fig. 2(a) uses of a 2x2 grid of MNIST digits. |
| Dataset Splits | No | We detect convergence by a rapid increase in the accuracy on a validation set (typically occurring after around 30k training examples). However, the paper does not specify the size or exact splitting methodology of this validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'TensorFlow (Abadi et al., 2015)' but does not provide a specific version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | We have a separate learning rate for the perceptual networks in both the MTNN baseline and NTPT which is 100 fold smaller than the learning rate for the task-specific parts. |