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.
The Computational Complexity of Counting Linear Regions in ReLU Neural Networks
Authors: Moritz Stargalla, Christoph Hertrich, Daniel Reichman
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 19,462 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,761 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 | Our paper is of theoretical nature and we strive towards a thorough understanding of the problem of counting regions from a computational complexity perspective. |
| Researcher Affiliation | Academia | Moritz Stargalla University of Technology Nuremberg EMAIL Christoph Hertrich University of Technology Nuremberg EMAIL Daniel Reichman Worcester Polytechnic Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 SEARCHAFFINEPIECE Input: A Re LU network N and a vector (a1, . . . , an, b) Qn+1. Output: 1 if Pn i=1 aixi + b is a function of an affine region of N, else 0. 1: for a {0, 1}s(N) do 2: if dim Sa = n then (Lemma A.5) 3: if Pn i=1 aixi + b = f a N(x) then return 1 (Lemma A.4) return 0 Algorithm 2 EXHAUSTIVESEARCH Input: A Re LU network N. Output: Number of affine regions of N. 1: nmax = max{n0, n1, . . . , nd+1} 2: U = 236d2n2 max Amax (Lemma A.3) 3: R = 0 4: for (a, b) { U, . . . , U}n+1 {1, . . . , U}n+1 do 5: if gcd(ai, bi) = 1 for i [n + 1] then 6: R R + SEARCHAFFINEPIECE(N, ( a1 b1 , . . . , an+1 bn+1 )) (Lemma B.4) return R |
| Open Source Code | No | Justification: No experiments. |
| Open Datasets | No | Justification: No experiments. |
| Dataset Splits | No | Justification: No experiments. |
| Hardware Specification | No | Justification: No experiments. |
| Software Dependencies | No | Justification: No experiments. |
| Experiment Setup | No | Justification: No experiments. |