Bayesian Structural Adaptation for Continual Learning
Authors: Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on supervised and unsupervised benchmarks demonstrate that our approach performs comparably or better than recent advances in continual learning. 5. Experiments We perform experiments on both supervised and unsupervised continual learning scenarios. We also evaluate our model on task-agnostic setup for unsupervised CL and compare our method with relevant state-of-the-art methods. In addition to the quantitative (accuracy/log-likelihood comparisons) and qualitative (generation) results, we also examine the network structures learned by our model. |
| Researcher Affiliation | Collaboration | 1Microsoft, India 2SAP Labs, India 3Department of Computer Science, IIT Kanpur, India. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The code for our models can be found at this link: https: //github.com/npbcl/icml21 |
| Open Datasets | Yes | We perform our evaluations on five supervised CL benchmarks: Split MNIST, Split not MNIST(small), Permuted MNIST, Split fashion MNIST and Split Cifar100. For MNIST, the tasks are sequence of single digit generation from 0 to 9. Similarily, for not MNIST each task is single character generation from A to J. |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details or percentages for a validation split, nor does it refer to predefined validation splits with citations. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or processing power) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not explicitly provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |