Reproducibility Index

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..

International Conference on Learning Representations (ICLR) - 2014

Documentation Rate of Empirical Papers by Reproducibility Variable

Distribution of Empirical Papers by Number of Documented Variables

Website:

Venue Year Papers
Reproducibility Score Reproducibility Score based on Gundersen et al. (2025). See Methods for details.
Documentation Score Documentation Score is the average score over the seven reproducibility variables for empirical research papers. See Methods for details.
% Empirical Percentage of papers that are empirical research vs theoretical research.
% Industry Percentage of empirical research papers with at least one author from Industry.
Website
ICLR 2014 38 0.39 3.16 97.37% 27.03%
Pseudocode
Open Source Code
Open Datasets
Dataset Splits
Hardware Specification
Software Dependencies
Experiment Setup
A Generative Product-of-Filters Model of Audio ❌ ✅ ✅ ❌ ❌ ❌ ✅ 3
An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks ❌ ✅ ✅ ❌ ❌ ❌ ✅ 3
An empirical analysis of dropout in piecewise linear networks ❌ ❌ ✅ ✅ ❌ ❌ ✅ 3
Auto-Encoding Variational Bayes ✅ ❌ ✅ ❌ ✅ ❌ ✅ 4
Bounding the Test Log-Likelihood of Generative Models ✅ ❌ ✅ ❌ ❌ ❌ ✅ 3
Deep Convolutional Ranking for Multilabel Image Annotation ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling ❌ ✅ ❌ ✅ ❌ ❌ ✅ 3
EXMOVES: Classifier-based Features for Scalable Action Recognition ✅ ✅ ✅ ✅ ✅ ❌ ✅ 6
End-to-End Text Recognition with Hybrid HMM Maxout Models ✅ ❌ ✅ ❌ ❌ ❌ ✅ 3
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
Fast Training of Convolutional Networks through FFTs ❌ ❌ ✅ ❌ ✅ ✅ ✅ 4
Group-sparse Embeddings in Collective Matrix Factorization ✅ ❌ ✅ ✅ ❌ ❌ ✅ 4
How to Construct Deep Recurrent Neural Networks ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
Intriguing properties of neural networks ❌ ❌ ✅ ✅ ❌ ❌ ✅ 3
Learned versus Hand-Designed Feature Representations for 3d Agglomeration ❌ ❌ ❌ ❌ ❌ ❌ ✅ 1
Learning Human Pose Estimation Features with Convolutional Networks ❌ ❌ ✅ ❌ ✅ ❌ ❌ 2
Learning Semantic Script Knowledge with Event Embeddings ✅ ❌ ✅ ✅ ❌ ❌ ✅ 4
Learning Transformations for Classification Forests ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
Learning to encode motion using spatio-temporal synchrony ❌ ❌ ✅ ✅ ✅ ❌ ✅ 4
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
Multilingual Distributed Representations without Word Alignment ❌ ✅ ✅ ✅ ❌ ❌ ✅ 4
Network In Network ❌ ❌ ✅ ✅ ❌ ❌ ❌ 2
Neuronal Synchrony in Complex-Valued Deep Networks ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
On Fast Dropout and its Applicability to Recurrent Networks ❌ ❌ ✅ ✅ ❌ ❌ ✅ 3
On the number of inference regions of deep feed forward networks with piece-wise linear activations ❌ ❌ ❌ ❌ ❌ ❌ ❌ 0
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks ✅ ✅ ✅ ✅ ✅ ❌ ✅ 6
Relaxations for inference in restricted Boltzmann machines ✅ ❌ ✅ ❌ ❌ ❌ ✅ 3
Revisiting Natural Gradient for Deep Networks ✅ ✅ ✅ ✅ ✅ ❌ ✅ 6
Sequentially Generated Instance-Dependent Image Representations for Classification ✅ ❌ ✅ ✅ ❌ ❌ ✅ 4
Some Improvements on Deep Convolutional Neural Network Based Image Classification ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
Sparse similarity-preserving hashing ❌ ❌ ✅ ✅ ❌ ❌ ✅ 3
Spectral Networks and Locally Connected Networks on Graphs ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
The return of AdaBoost.MH: multi-class Hamming trees ✅ ❌ ✅ ✅ ❌ ✅ ✅ 5
Unsupervised Feature Learning by Deep Sparse Coding ❌ ❌ ✅ ✅ ❌ ❌ ✅ 3
Zero-Shot Learning and Clustering for Semantic Utterance Classification ❌ ❌ ❌ ✅ ✅ ❌ ✅ 3
Zero-Shot Learning by Convex Combination of Semantic Embeddings ❌ ❌ ✅ ❌ ❌ ❌ ✅ 2
k-Sparse Autoencoders ✅ ❌ ✅ ✅ ✅ ❌ ✅ 5