Compositional Kernel Machines
Authors: Robert Gens, Pedro Domingos
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we define CKMs, explore their properties, and present promising results on NORB datasets. Experiments show that CKMs can outperform SVMs and be competitive with convnets in a number of dimensions, by learning symmetries and compositional concepts from fewer samples without data augmentation. |
| Researcher Affiliation | Academia | Robert Gens & Pedro Domingos Department of Computer Science & Engineering University of Washington Seattle, WA 98195, USA {rcg,pedrod}@cs.washington.edu |
| Pseudocode | No | The paper describes algorithms and procedures in prose and with mathematical formulations but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | No | The paper does not provide a link to its own source code for the described methodology or make a clear statement about its availability. It only refers to a third-party library, TensorFlow, which is a general framework, not their specific implementation. |
| Open Datasets | Yes | We test CKMs on three image classification scenarios that feature images from either the small NORB dataset or the NORB jittered-cluttered dataset (Le Cun et al., 2004). |
| Dataset Splits | Yes | In this experiment, we partition the training set of NORB jittered-cluttered into a new dataset with 10% withheld for each of validation and testing. |
| Hardware Specification | No | The paper mentions running experiments on 'CPU' and 'GPU' ('CKM on a CPU' and 'convnets trained for much longer on a GPU') but does not specify any particular models (e.g., Intel Core i7, NVIDIA Tesla V100) or detailed hardware configurations. |
| Software Dependencies | No | The paper mentions 'Convnets and their features are computed using the Tensor Flow library (Abadi et al., 2015)' but does not provide a specific version number for TensorFlow or any other software dependency. |
| Experiment Setup | Yes | The hyperparameters of ORB feature extraction, leaf kernels, cost function, and optimization were chosen using grid search on a validation set. |