Bayesian Representation Learning with Oracle Constraints
Authors: Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch
ICLR 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Sections 4 and 5, we present experimental results for benchmarking the proposed approaches, illustrating their properties and discussing the benefits they confer over competing approaches. |
| Researcher Affiliation | Academia | Theofanis Karaletsos Computational Biology, Sloan Kettering Institute 1275 York Avenue, New York, USA Theofanis.Karaletsos@ratschlab.org Serge Belongie Cornell Tech 111 8th Avenue #302, New York, USA sjb344@cornell.edu Gunnar R atsch Computational Biology, Sloan Kettering Institute 1275 York Avenue, New York, USA Gunnar.Ratsch@ratschlab.org |
| Pseudocode | No | The paper describes the generative model and learning process in textual steps and mathematical formulations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using open-source libraries like Theano, but it does not provide an explicit statement or link indicating that the source code for the methodology developed in this paper is publicly available. |
| Open Datasets | Yes | We use a relatively small dataset that is, however, well-suited to illustrate the features of the algorithm and facilitates the interpretation of the factorized latent spaces: the Yale Faces dataset (Lee et al., 2005). |
| Dataset Splits | No | We split it into 300 test images and 2, 114 training images. (The paper explicitly mentions training and testing splits for the Yale Faces dataset, but it does not specify a separate validation split or how it was used for hyperparameter tuning or model selection.) |
| Hardware Specification | No | All experiments were run on Graphics Processing Units (GPUs) using a theano (Bergstra et al., 2010) implementation and did not take more than a few hours each. (The paper mentions 'GPUs' but does not specify any particular GPU models, CPU details, or other specific hardware components used for the experiments.) |
| Software Dependencies | No | In all experiments we used diagonal Normal distributions as priors for the latent space and rms Prop with momentum (Graves, 2013) or ADAM (Kingma & Ba, 2014) as an optimizer. All experiments were run on Graphics Processing Units (GPUs) using a theano (Bergstra et al., 2010) implementation and did not take more than a few hours each. (The paper lists several software components and optimizers, but it does not provide specific version numbers for them, e.g., 'theano 0.7' or 'ADAM 1.0'.) |
| Experiment Setup | Yes | We proceed to learn fully unsupervised models of these images using an architecture with 200 hidden deterministic units and 50 latent variables. The deterministic layers use tanh nonlinearities. |