A Constraint-Based Approach to Learning and Explanation
Authors: Gabriele Ciravegna, Francesco Giannini, Stefano Melacci, Marco Maggini, Marco Gori3658-3665
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation is provided to support the proposed approach, in which we also explore the regularization effects introduced by the proposed Information Based Learning of Constraint (IBLC) algorithm. |
| Researcher Affiliation | Academia | 1Department of Information Engineering (DIINFO), University of Florence, Florence, Italy gabriele.ciravegna@unifi.it 2Department of Information Engineering and Science (DIISM), University of Siena, Siena, Italy {fgiannini, mela, maggini, marco}@diism.unisi.it |
| Pseudocode | Yes | Algorithm 1 Stage-based Optimization procedure (superscripts indicate the iteration number). |
| Open Source Code | Yes | All experiments are reproducible by using the code available at https://github.com/gabrieleciravegna/Information-based-Constrained-Learning |
| Open Datasets | Yes | The datasets can be downloaded at http://yann.lecun.com/ exdb/mnist/ and https://www.cs.stanford.edu/ roozbeh/pascal-parts/pascal-parts.html |
| Dataset Splits | Yes | Training, validation and test sets are composed of 20k, 5k, and 10k digits, respectively, taken from a (class-balanced) subset of the MNIST data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions the 'Adam optimizer' but does not specify version numbers for it or any other software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The model parameters have been cross-validated ranging them in the following grids: learning rate of the Adam optimizer {0.01, 0.001, 0.0001}, γψ {1e 2, 1e 3, 1e 4}, γf = 1e 6 (we used the squared norm of the weights to implement f ). We also considered different scaling factors {0.5, 1, 2} of the penalty term associated to the MI (Eq. 4), and a convex combination of the two entropy terms, modulated by a coefficient {0.25, 0.5, 0.75}. All experiments are based on 1000 epochs. For the stage-based optimization, the task function learning stage lasts 200 epochs, while the constraint learning stage lasts 50 epochs. |