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..
Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks
Authors: Wenlin Chen, Hong Ge
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section contains empirical evaluation of Gm P with neural network architectures of different sizes on both illustrative demonstrations and more challenging machine learning classification and regression benchmarks. |
| Researcher Affiliation | Academia | Wenlin Chen University of Cambridge MPI for Intelligent Systems EMAIL Hong Ge University of Cambridge EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Wenlin-Chen/geometric-parameterization. |
| Open Datasets | Yes | We evaluate Gm P on 7 regression problems from the UCI dataset [11]. We evaluate Gm P with a medium-sized convolutional neural network VGG-6 [58] on Image Net32 [8] We evaluate Gm P with a large residual neural network, Res Net-18 [22], on the full Image Net (ILSVRC 2012) dataset [10]. |
| Dataset Splits | Yes | We train an MLP with one hidden layer and 100 hidden units for 10 different random 80/20 train/test splits. Image Net (ILSVRC 2012) dataset [10], which consists of 1,281,167 training images and 50,000 validation images. |
| Hardware Specification | Yes | All models are trained on a single NVIDIA Ge Force RTX 2080 Ti. All models are trained on a single NVIDIA A100 (80GB). |
| Software Dependencies | No | The paper mentions using optimizers like Adam and SGD and implies a deep learning framework, but it does not provide specific version numbers for any software dependencies (e.g., PyTorch version, Python version, CUDA version). |
| Experiment Setup | Yes | We use the Adam optimizer [28] with full-batch training. We use cross-validation to select the learning rate for each compared method from the set {0.001, 0.003, 0.01, 0.03, 0.1, 0.3}. We find that the optimal initial learning rate is 0.1 for Gm P and 0.01 for all the other compared methods. |