Online Meta-Learning
Authors: Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation on three different largescale problems suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches. |
| Researcher Affiliation | Academia | 1UC Berkeley 2University of Washington. Correspondence to: Chelsea Finn <cbfinn@stanford.edu>, Aravind Rajeswaran <aravraj@cs.washington.edu>. |
| Pseudocode | Yes | Algorithm 1 Online Meta-Learning with FTML |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The experiments involve vision-based sequential learning tasks with the MNIST, CIFAR-100, and PASCAL 3D+ datasets. |
| Dataset Splits | No | The paper mentions 'held-out data Dtest_t' for evaluation and 'meta-training tasks' but does not specify explicit percentages or sample counts for train/validation/test splits, nor does it explicitly detail a 'validation' set split. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory, or specific computing platforms) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2015)', 'standard automatic differentiation libraries', and the 'Mu Jo Co physics engine (Todorov et al., 2012)' but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | For Rainbow MNIST, 'we set 90% classification accuracy as the proficiency threshold.' For pose prediction, 'set the proficiency threshold to an error of 0.05.' It also mentions 'Hyperparameters parameters ,', and that 'we meta-train with update minibatches of size at-most 25'. |