Neural network gradient-based learning of black-box function interfaces
Authors: Alon Jacovi, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Jonathan Berant
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present four experiments in increasing complexity to test the Estimate and Replace approach and compare its performance against existing solutions. Specifically, the experiments demonstrate that by leveraging external black-box functions, we achieve better generalization and better learning efficiency in comparison with existing competing solutions, without using intermediate labels. |
| Researcher Affiliation | Collaboration | Alon Jacovi1,2 , Guy Hadash1 , Einat Kermany1 , Boaz Carmeli1 , Ofer Lavi1, George Kour1, Jonathan Berant3,4 1 IBM Research, Israel 2 Bar Ilan University, Israel 3 Tel Aviv University, Israel 4 Allen Institute for Artificial Intelligence |
| Pseudocode | No | The paper describes the training procedures and architecture in prose and diagrams, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing its code or provide a link to a code repository. |
| Open Datasets | Yes | The Image-Addition and Image-Lookup tasks use the MNIST training and test sets. |
| Dataset Splits | No | The paper mentions 'training and test sets' for MNIST and TLL, but does not explicitly specify a distinct validation dataset split or how data is partitioned for validation purposes to reproduce the experiments. |
| Hardware Specification | No | The paper states 'The implementation was done in Py Torch' but does not provide any specific hardware details such as GPU/CPU models or cloud instances used for the experiments. |
| Software Dependencies | No | The paper mentions using PyTorch, TensorFlow, A3C, and Adam optimization but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | The hyper-parameters of the model are in Table 6. |