RanDumb: Random Representations Outperform Online Continually Learned Representations
Authors: Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru, Philip Torr, Ozan Sener, Puneet Dokania
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |