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..
The Hessian perspective into the Nature of Convolutional Neural Networks
Authors: Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our upper bounds in a variety of settings, in particular, with both linear and Re LU activations, MSE and CE loss, as well as on datasets such as CIFAR10, Fashion MNIST, MNIST, and a synthetic dataset. |
| Researcher Affiliation | Academia | 1ETH Zürich, Switzerland 2MPI for Intelligent Systems, Tübingen, Germany. |
| Pseudocode | No | The paper describes mathematical proofs, theorems, and derivations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The corresponding code can be found under https://github.com/sidak/hessian_perspective_cnns. |
| Open Datasets | Yes | We empirically validate our upper bounds in a variety of settings, in particular, with both linear and Re LU activations, MSE and CE loss, as well as on datasets such as CIFAR10, Fashion MNIST, MNIST, and a synthetic dataset. |
| Dataset Splits | No | The paper mentions using datasets like CIFAR10, but it does not specify explicit training, validation, or test splits (e.g., 80/10/10 split percentages or sample counts for each set) needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. It only mentions general experimental settings. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow, specific solvers or libraries) used in their experiments, which is necessary for reproducibility. |
| Experiment Setup | Yes | We empirically validate our upper bounds in a variety of settings, in particular, with both linear and Re LU activations, MSE and CE loss, as well as on datasets such as CIFAR10, Fashion MNIST, MNIST, and a synthetic dataset. Following (Singh et al., 2021), to rigorously illustrate the match with our bounds, we compute the exact Hessians, without approximations, and in Float64 precision. |