Information Retention via Learning Supplemental Features
Authors: Zhipeng Xie, Yahe Li
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 EXPERIMENTS This section is devoted to a thorough empirical study of the proposed Info R-LSF method |
| Researcher Affiliation | Academia | Zhipeng Xie*, Yahe Li* (* Equal Contribution) School of Computer Science, Fudan University, Shanghai 200434, China xiezp@fudan.edu.cn, yaheli21@m.fudan.edu.cn |
| Pseudocode | Yes | C ALGORITHM TABLE OF INFOR-LSF |
| Open Source Code | Yes | 1Code available at https://github.com/liyahe/Info R-LSF |
| Open Datasets | Yes | We use two image classification datasets, CIFAR10 (Krizhevsky et al., 2009) and CIFAR100(Krizhevsky et al., 2009). We use two sentiment analysis datasets, namely IMDB (Maas et al., 2011) and YELP (Zhang et al., 2015). |
| Dataset Splits | Yes | Table 7: Datasets used in experiments. |
| Hardware Specification | No | The paper provides computational complexity analysis in Section A.4 and Table 9 with running times, but it does not specify any particular hardware components such as GPU models, CPU types, or memory used for these experiments or for training. |
| Software Dependencies | No | The paper mentions optimizers (SGD, AdamW) and backbone networks (ResNet-18, BERTBASE) and refers to a PyTorch implementation in a footnote, but it does not specify exact version numbers for any software libraries or dependencies (e.g., 'PyTorch 1.x' or 'Python 3.x'). |
| Experiment Setup | Yes | For CIFAR10, ... train the model for 200 epochs with batch size 256, initial learning rate 0.1 and weight decay 5e-4. We apply SGD optimizer with momentum 0.9 and a step scheduler that decays the learning rate by 0.1 every 160 epochs. |