QLABGrad: A Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning
Authors: Minghan Fu, Fang-Xiang Wu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on multiple architectures, including MLP, CNN, and Res Net, on MNIST, CIFAR10, and Image Net datasets, demonstrate that QLABGrad outperforms various competing schemes for deep learning. |
| Researcher Affiliation | Academia | Minghan Fu1, Fang-Xiang Wu1,2 1Department of Mechanical Engineering, University of Saskatchewan 2Department of Computer Science, University of Saskatchewan |
| Pseudocode | Yes | Algorithm 1: QLABGrad |
| Open Source Code | No | The paper does not provide a direct link to a source code repository or an explicit statement about the release of the code for the described methodology. |
| Open Datasets | Yes | To assess the effectiveness of our proposed QLABGrad, we carry out extensive experiments on three different datasets (MNIST (Le Cun et al. 1998), CIFAR10 (Krizhevsky, Hinton et al. 2009), and Tiny-Image Net (Le and Yang 2015)) with various models (multi-layer neural network, CNN, and Res Net-18), comparing to various popular competing schemes, including basic SGD, RMSProp, Ada Grad, and Adam. |
| Dataset Splits | No | The MNIST dataset (Le Cun et al. 1998) includes 60,000 training and 10,000 testing images. [...] The CIFAR-10 dataset (Krizhevsky, Hinton et al. 2009) comprises 50,000 training images and 10,000 test images... (Only training and testing splits are mentioned, no explicit validation split.) |
| Hardware Specification | Yes | All experiments are performed on a single RTX 3090 GPU. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The MNIST dataset (Le Cun et al. 1998) includes 60,000 training and 10,000 testing images. Figures 4 and 5 show the training loss for a multi-layer neural network (MLP) and a convolution neural network (CNN), respectively, with a mini-batch size of 64. |