Differentiable Programs with Neural Libraries
Authors: Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 <algaunt@microsoft.com>. |
| 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. |