Packed Ensembles for efficient uncertainty estimation
Authors: Olivier Laurent, Adrien Lafage, Enzo Tartaglione, Geoffrey Daniel, Jean-marc Martinez, Andrei Bursuc, Gianni Franchi
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive research indicates that PE accurately preserves the properties of DE, such as diversity, and performs equally well in terms of accuracy, calibration, out-of-distribution detection, and robustness to distribution shift. We make our code available at github.com/ENSTAU2IS/torch-uncertainty. and 4 EXPERIMENTS To validate the performance of our method, we conduct experiments on classification tasks and measure the influence of the parameters α and γ. |
| Researcher Affiliation | Collaboration | Olivier Laurent,1,2,* Adrien Lafage,2,* Enzo Tartaglione,3 Geoffrey Daniel,1 Jean-Marc Martinez,1 Andrei Bursuc4 & Gianni Franchi2, Universit e Paris-Saclay, CEA, SGLS,1 U2IS, ENSTA Paris, Institut Polytechnique de Paris,2 LTCI, T el ecom Paris, Institut Polytechnique de Paris,3 valeo.ai4 |
| Pseudocode | No | The paper describes mathematical formulations and implementation details (e.g., equations for convolutional layers, architectural diagrams in Figure 3) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make our code available at github.com/ENSTAU2IS/torch-uncertainty. and To further promote accessibility, we have created an open-source pip-installable Py Torch package, torch-uncertainty, that includes Packed-Ensembles layers. |
| Open Datasets | Yes | First, we demonstrate the efficiency of Packed-Ensembles on CIFAR-10 and CIFAR100 (Krizhevsky, 2009), showing how the method adapts to tasks of different complexities. and Second, we report our results for Packed-Ensembles on Image Net (Deng et al., 2009), which we compare against all baselines. |
| Dataset Splits | Yes | First, we demonstrate the efficiency of Packed-Ensembles on CIFAR-10 and CIFAR100 (Krizhevsky, 2009)... and Second, we report our results for Packed-Ensembles on Image Net (Deng et al., 2009)... and We use accuracy as the validation criterion (i.e., the final trained model is the one with the highest accuracy). |
| Hardware Specification | Yes | Most training instances are completed on a single Nvidia RTX 3090 except for Image Net, for which we use 2 to 8 Nvidia A100-80GB. |
| Software Dependencies | Yes | Table 8: Comparison of training and inference times of different ensemble techniques using torch1.12.1+cu113 on an RTX 3090. |
| Experiment Setup | Yes | Table 4: Hyperparameters for image classification experiments. HFlip denotes the classical horizontal flip. and General Considerations. Table 4 summarizes all the hyperparameters used in the paper for CIFAR-10 and CIFAR-100. In all cases, we use SGD combined with a multistep-learning-rate scheduler multiplying the rate by γ-lr at each milestone. |