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..
On Robust Concepts and Small Neural Nets
Authors: Amit Deshpande, Sushrut Karmalkar
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that any noise-stable boolean function on n boolean-valued input variables can be well-approximated by a two-layer linear threshold circuit with a small number of hidden-layer nodes and small weights, that depend only on the noise-stability and approximation parameters, and are independent of n. We also give a polynomial time learning algorithm that outputs a small two-layer linear threshold circuit that approximates such a given function. The universal approximation theorem of Hornik et al. (1989) and Cybenko (1992) provides a foundation to the mathematical theory of arti๏ฌcial neural networks. |
| Researcher Affiliation | Collaboration | Amit Deshpande Microsoft Research, Vigyan, 9 Lavelle Road, Bengaluru 560001, India EMAIL Sushrut Karmalkar Department of Computer Science, The University of Texas at Austin, 2317 Speedway, Stop D9500 Austin, TX 78712, USA EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not mention specific datasets used for training or public availability of any dataset. |
| Dataset Splits | No | This is a theoretical paper and does not provide specific dataset split information. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include specific experimental setup details, hyperparameters, or training configurations. |