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..
Dynamic Inference with Neural Interpreters
Authors: Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate Neural Interpreters in a variety of problem settings. |
| Researcher Affiliation | Collaboration | 1Max-Planck-Institute for Intelligent Systems, Tübingen. 2Mila, Québec. 3Amazon Web Services. Work partially done during an internship at Amazon Web Services. |
| Pseudocode | No | The paper describes the architecture and its components using mathematical equations and text, but does not include any explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code or a link to a code repository. |
| Open Datasets | Yes | We consider three related datasets sharing the same label semantics, namely SVHN [41], MNISTM [20] and MNIST [35]... In addition, we also use unaugmented samples from the K-MNIST dataset [12] of Hiragana characters to probe fast-adaptation to new data. |
| Dataset Splits | Yes | For each function, we sample 163840 points from a uniform distribution over [0, 1]5, of which we use 80% for training, and the remaining for validation. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU, CPU models, or cloud computing instances with detailed specifications) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions software like 'Pytorch image models' [60] and 'GELUs' [26] (an activation function) and frameworks like PyTorch, but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We pre-train a Neural Interpreter to regress all 20 functions simultaneously... train their outputs to regress the respective function. Having pre-trained the model for 20 epochs, we finetune it for an additional 3 epochs... We train Neural Interpreters for 100 epochs on the combined digits dataset... we finetune the model on K-MNIST with varying numbers of samples for 10 epochs. |