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..
Model Sensitivity Aware Continual Learning
Authors: Zhenyi Wang, Heng Huang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted across multiple datasets demonstrate the efficacy and versatility of our proposed method. |
| Researcher Affiliation | Academia | Zhenyi Wang and Heng Huang Department of Computer Science, Institute of Health Computing University of Maryland College Park College Park, MD, 20742 EMAIL;EMAIL |
| Pseudocode | Yes | Algorithm 1 Model Sensitivity Aware Continual Learning |
| Open Source Code | No | Justification: Code will be released. |
| Open Datasets | Yes | We conduct experiments on several datasets, including CIFAR10 (10 classes), CIFAR100 (100 classes) [29], and Tiny-Image Net (200 classes) [80], to assess the effectiveness of MACL in task incremental learning (Task-IL) and class incremental learning (Class-IL). |
| Dataset Splits | No | Following [7, 14], the hyperparameter is determined through the validation sets split from the training sets from the first three tasks. |
| Hardware Specification | Yes | We use a single NVIDIA A5000 GPU with 24GB memory to run the experiments. |
| Software Dependencies | No | The paper mentions using 'standard SGD optimizer' but does not specify versions for programming languages, libraries (e.g., PyTorch, TensorFlow), or other software dependencies. |
| Experiment Setup | Yes | For the hyperparameters in our method, we set α = 1.0 across all the datasets to minimize the model’s dependence on hyperparameters. For η, we set η = 1e 5 for CIFAR10 and CIFAR100, and η = 1e 6 for Tiny-Image Net. The η is selected from the range of [1e 4, 1e 5, 1e 6, 1e 7]. ... The batch size and replay buffer batch size are set to 32. |