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..
RanDumb: Random Representations Outperform Online Continually Learned Representations
Authors: Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru, Philip Torr, Ozan Sener, Puneet Dokania
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations, summarized in Table 1 (left, top), reveal that despite replacing the representation learning with a pre-defined random representation, Ran Dumb surpasses current stateof-the-art methods in latest online continual learning benchmarks [76]. |
| Researcher Affiliation | Collaboration | 1University of Oxford 2IIIT Hyderabad 3Apple |
| Pseudocode | No | The paper includes equations and figures (e.g., Figure 1, Figure 2) that illustrate the method, but it does not contain a formal pseudocode block or algorithm description. |
| Open Source Code | Yes | https://github.com/drimpossible/RanDumb |
| Open Datasets | Yes | We evaluate Ran Dumb using five datasets: MNIST, CIFAR10, CIFAR100, Tiny Image Net200, and mini Image Net100. |
| Dataset Splits | Yes | We use standard datasets and splits, we provide hyperparameters in experimental details along with ablations in experiment sections to understand the contribution of each component in our algorithm. |
| Hardware Specification | Yes | All experiments were conducted on a CPU server with a153 48-core Intel Xeon Platinum 8268 CPU and 392GB of RAM, requiring less than 30 minutes per experiment. |
| Software Dependencies | No | The paper mentions using 'Scikit-Learn implementation of Random Fourier Features' and 'LAMDA-PILOT codebase', but it does not specify the version numbers for these software components. |
| Experiment Setup | Yes | We used the Scikit-Learn implementation of Random Fourier Features [52] with 25K embedding size, γ = 1.0. We use progressively increasing ridge regression parameter (λ) with dataset complexity, λ = 10 6 for MNIST, λ = 10 5 for CIFAR10/100 and λ = 10 4 for Tiny Image Net200/mini Image Net100. |