Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Memorization Capacity of Neural Networks with Conditional Computation
Authors: Erdem Koyuncu
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,410 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,364 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the fundamental limits of neural conditional computation from the perspective of memorization capacity. |
| Researcher Affiliation | Academia | Erdem Koyuncu Department of Electrical and Computer Engineering University of Illinois Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1 An example conditional neural network |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that defines a dataset conceptually (X = {x1, . . . , xn} Rp) but does not use or provide access information for a publicly available empirical dataset for training. |
| Dataset Splits | No | This is a theoretical paper and does not describe empirical experiments involving validation sets. |
| Hardware Specification | No | This is a theoretical paper and does not describe any empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not describe any empirical experiments, thus no software dependencies with version numbers are listed. |
| Experiment Setup | No | This is a theoretical paper and does not describe any empirical experiments or their setup, including hyperparameters or training settings. |