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..
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators
Authors: Scott Lowe, Robert Earle, Jason d'Eon, Thomas Trappenberg, Sageev Oore
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We deploy these new activation functions, both in isolation and in conjunction to demonstrate their effectiveness on a variety of tasks including tabular classification, image classification, transfer learning, abstract reasoning, and compositional zero-shot learning. |
| Researcher Affiliation | Collaboration | Scott C. Lowe1,2, Robert Earle1,2, Jason d Eon1,2, Thomas Trappenberg1, Sageev Oore1,2 1Faculty of Computer Science Dalhousie University Halifax, Nova Scotia Canada 2Vector Institute for Artificial Intelligence Toronto, Ontario Canada |
| Pseudocode | No | The paper defines functions mathematically (e.g., ANDAIL(x, y) := { x + y, x < 0, y < 0; min(x, y), otherwise }), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our python package which provides an implementation of these the activation functions is available at https://github.com/DalhousieAI/pytorch-logit-logic, which is also available as PyPI package pytorch-logit-logic. |
| Open Datasets | Yes | The Bach Chorale dataset (Boulanger-Lewandowski et al., 2012) consists of 382 chorales composed by JS Bach... We tasked 2-layer MLPs with determining whether a short four-part musical excerpt is taken from a Bach chorale." and "We trained 2-layer MLP and 6-layer CNN models on MNIST with ADAM (Kingma & Ba, 2015)... |
| Dataset Splits | Yes | hyperparameters tuned through a random search against a validation set comprised of the last 10k images of the training partition. |
| Hardware Specification | Yes | Additionally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. |
| Software Dependencies | Yes | For a comprehensive set of baselines, we compared against every activation function built into Py Torch 1.10 (see Appendix A.17). |
| Experiment Setup | Yes | We trained 2-layer MLP and 6-layer CNN models on MNIST with ADAM (Kingma & Ba, 2015), 1-cycle schedule (Smith & Topin, 2017; Smith, 2018), and using hyperparameters tuned through a random search against a validation set comprised of the last 10k images of the training partition. |