Weakly Supervised Regression with Interval Targets
Authors: Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various datasets demonstrate the effectiveness of our proposed method. In this section, we conduct extensive experiments to validate the effectiveness of our proposed limiting method. |
| Researcher Affiliation | Academia | 1College of Computer Science, Chongqing University, China 2School of Computer Science and Engineering, Nanyang Technological University, Singapore 3College of Computer Science and Technology, Jilin University, China. |
| Pseudocode | No | The paper describes its methods through text and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Datasets. We conduct experiments on nine datasets, including two computer vision datasets (Age DB (Moschoglou et al., 2017) and IMDB-WIKI (Rothe et al., 2018)), one natural language processing dataset (STS-B (Cer et al., 2017)), and six datasets from the UCI Machine Learning Repository (Dua & Graff, 2017)... |
| Dataset Splits | Yes | Then we randomly split each dataset into training, validation, and test sets by the proportions of 60%, 20%, and 20%, respectively. |
| Hardware Specification | No | The paper specifies the neural network architectures (e.g., ResNet-50, MLP) and optimizers used, but it does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components such as the Adam optimizer, ResNet-50 backbone, and GloVe word embeddings, but it does not specify version numbers for these or any other software libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | For the linear model and the MLP model, we use the Adam optimization method (Kingma & Ba, 2015) with the batch size set to 512 and the number of training epochs set to 1,000, and the learning rate for all methods is selected from {10-2, 10-3}. We use the Adam optimizer to train all methods for 100 epochs with an initial learning rate of 10-3 and fix the batch size to 256. We also use the Adam optimizer to train all methods for 10-4 and fix the batch size to 256. |