Understanding Trainable Sparse Coding with Matrix Factorization
Authors: Thomas Moreau, Joan Bruna
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 3 presents the generic architectures that will enable the usage of such schemes and the numerical experiments, which validate our analysis over a range of different scenarios. |
| Researcher Affiliation | Academia | Thomas Moreau CMLA, ENS Cachan, CNRS, Universit e Paris-Saclay, 94235 Cachan, France thomas.moreau@cmla.ens-cachan.fr Joan Bruna Courant Institute of Mathematical Sciences, New York University , New York, NY 10012, USA joan.bruna@berkeley.edu |
| Pseudocode | No | The paper describes algorithms using mathematical equations and descriptions of steps, such as (16) and (18), but does not present them in a formal pseudocode block or explicitly labeled algorithm section. |
| Open Source Code | Yes | The code to reproduce the figures is available online2. 2The code can be found at https://github.com/tom Moral/Adaptive Optim |
| Open Datasets | Yes | a highly structured dictionary composed of translation invariant Haar wavelets is used to encode 8x8 patches of images from the PASCAL VOC 2008 dataset. LISTA was used to encode MNIST images over an unconstrained dictionary |
| Dataset Splits | No | The paper mentions using training and test sets from standard datasets like PASCAL VOC 2008 and MNIST, but does not provide specific train/validation/test split percentages or details for a distinct validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | All the experiments were run using Python and Tensorflow. For all the experiments, the training is performed using Adagrad (Duchi et al., 2011). The dictionary of 100 atoms was learned from 10000 MNIST images in grayscale rescaled to 17x17 using the implementation of Mairal et al. (2009) proposed in scikit-learn. |
| Experiment Setup | Yes | The values are set to m=100, n=64 for the dictionary dimension, ρ = 5/m for the sparsity level and σ=10 for the activation coefficient generation parameters. The sparsity regularization is set to λ=0.01. The training is performed using Adagrad (Duchi et al., 2011). The architecture ΦK Θ with parameters Θ = (W (k) g , W (k) e , θ(k))k=1,...K obtained by unfolding K times the recurrent network. The network Fac Net, ΨK Θ is formed using layers such that: zk+1 = ψk Θ(zk) := AThλS 1(Azk S 1A(DTDzk DTx)) , with S diagonal and A unitary, the parameters of the k-th layer. |