Deep Gamblers: Learning to Abstain with Portfolio Theory
Authors: Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R. Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method can identify uncertainty in data points, and achieves strong results on SVHN and CIFAR10 at various coverages of the data. |
| Researcher Affiliation | Academia | Institute for Physics of Intelligence & Department of Physics, University of Tokyo Machine Learning Department, Carnegie Mellon University Language Technologies Institute, Carnegie Mellon University {zliu,wang}@cat.phys.s.u-tokyo.ac.jp ueda@phys.s.u-tokyo.ac.jp {pliang,rsalakhu,morency}@cs.cmu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | Experiments show that our method can identify uncertainty in data points, and achieves strong results on SVHN and CIFAR10 at various coverages of the data. ... SVHN [31] (Table 3), CIFAR10 [23] (Table 4) and Cat vs. Dog (Table 5). |
| Dataset Splits | Yes | The best models of ours for a given coverage are chosen using a validation set, which is separated from the test set by a fixed random seed, and the best single model is chosen by using the model that achieves overall best validation accuracy. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or cloud instance specifications. |
| Software Dependencies | No | The paper mentions using a 'version of VGG16' but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | We use a version of VGG16 that is especially optimized for small datasets [27] with batchnorm and dropout. ... A grid search is done over hyperparameter o with a step size of 0.2. ... we train a network with 2 hidden layers each with 50 neurons and tanh activation. ... The model is a simple 4-layer CNN. |