Learning Multi-Modal Word Representation Grounded in Visual Context
Authors: Éloi Zablocki, Benjamin Piwowarski, Laure Soulier, Patrick Gallinari
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experiments and extensive analysis of the obtained results. Section 6 Experiments and Results. |
| Researcher Affiliation | Academia | Eloi Zablocki, Benjamin Piwowarski, Laure Soulier, Patrick Gallinari LIP6 UPMC Univ Paris 06, UMR 7606, CNRS, Sorbonne Universit es F-75005, Paris, France {eloi.zablocki, benjamin.piwowarski, laure.soulier, patrick.gallinari}@lip6.fr |
| Pseudocode | No | The paper describes its model and loss functions using mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | We use a large collection of English texts, a dump of the Wikipedia database (http://dumps.wikimedia.org/enwiki), cleaned and tokenized with the Gensim software ( ˇReh uˇrek and Sojka ). This provides us with 4.2 million articles, and a vocabulary of 2.1 million unique words. For visual data, we use the Visual Genome dataset (Krishna et al. 2017)... |
| Dataset Splits | Yes | The values of hyperparameters were found with cross-validation: λ = 0.1, μ = 0.1, γ = 0.5, α = 0.2. A linear SVM classifier is trained and 5-fold validation scores are reported. |
| Hardware Specification | No | The paper mentions passing images through a 'pre-trained Inception-V3 CNN' but does not specify any hardware details like GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'python and TensorFlow' and 'Gensim software', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use 5 negative examples per entity, and our models are trained with stochastic gradient descent with learning rate lr = 10 3 and mini-batches of size 64. N and M are regularized with a L2-penalty respectively weighted by scalars λ and μ. The values of hyperparameters were found with cross-validation: λ = 0.1, μ = 0.1, γ = 0.5, α = 0.2. |