Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer
Authors: XINYUE HU, Zhibin Duan, Bo Chen, Mingyuan Zhou
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that with enhanced disentanglement capabilities, BNDL not only improves the model s accuracy but also provides reliable uncertainty estimation and improved interpretability. (...) 5 EXPERIMENTS |
| Researcher Affiliation | Academia | 1 National Key Laboratory of Radar Signal Processing, Xidian University, Xi an, 710071, China. 2 School of Mathematics and Statistics, Xi an Jiaotong University, Xi an, Shaanxi, China. 3 Mc Combs School of Business, The University of Texas at Austin, Austin, TX 78712 |
| Pseudocode | No | The paper describes the variational inference network and process using equations, but no explicit pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/XYHu122/BNDL. (...) The novel methods introduced in this paper are accompanied by detailed descriptions (Sec. 3), and their implementations are provided at https://github.com/XYHu122/BNDL. |
| Open Datasets | Yes | We assessed the effectiveness of BNDL across multiple datasets, including CIFAR-10, CIFAR-100, and Image Net-1k. (...) The CIFAR-10, CIFAR-100, and Image Net-1k datasets we used are all publicly available standard datasets. As for Places-10, it is a subset of Places365 (Zhou et al., 2017) containing the classes airport terminal , boat deck , bridge , butcher s shop , churchoutdoor , hotel room , laundromat , river , ski slope and volcano . |
| Dataset Splits | No | The paper mentions using "publicly available standard datasets" but does not explicitly state the dataset splits (e.g., train/validation/test percentages or sample counts) or cite a specific source for predefined splits. |
| Hardware Specification | Yes | All experiments are conducted on Linux servers equipped with 32 AMD EPYC 7302 16-Core Processors and 2 NVIDIA 3090 GPUs. |
| Software Dependencies | Yes | Models are implemented in Py Torch version 1.12.1, scikit-learn version 1.0.2 and Python 3.7. |
| Experiment Setup | Yes | Training from scratch setup For Res Net-18 on CIFAR-10 and CIFAR-100, we set the batch size to 128, learning rate to 0.1, training epochs to 150, and weight decay to 5e-4. For Res Net-50 on Image Net-1k, we set the batch size to 256, weight decay to 1e-4, epochs to 200, and learning rate to 0.1. (...) Fine-tuning setup For Places-10, we set the learning rate to 0.1, batch size to 128, and epochs to 100. For Image Net-1k, CIFAR-10, and CIFAR-100, we set the learning rate to 0.001 and epochs to 200. |